US20230157524A1 - Robotic systems and methods for navigation of luminal network that detect physiological noise - Google Patents
Robotic systems and methods for navigation of luminal network that detect physiological noise Download PDFInfo
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Definitions
- the systems and methods disclosed herein are directed to surgical robotics, and more particularly to endoluminal navigation.
- Bronchoscopy is a medical procedure that allows a physician to examine the inside conditions of a patient's lung airways, such as bronchi and bronchioles.
- the lung airways carry air from the trachea, or windpipe, to the lungs.
- a thin, flexible tubular tool known as a bronchoscope, may be inserted into the patient's mouth and passed down the patient's throat into his/her lung airways, and patients are generally anesthetized in order to relax their throats and lung cavities for surgical examinations and operations during the medical procedure.
- a bronchoscope can include a light source and a small camera that allows a physician to inspect a patient's windpipe and airways, and a rigid tube may be used in conjunction with the bronchoscope for surgical purposes, e.g., when there is a significant amount of bleeding in the lungs of the patient or when a large object obstructs the throat of the patient.
- a rigid tube may be used in conjunction with the bronchoscope for surgical purposes, e.g., when there is a significant amount of bleeding in the lungs of the patient or when a large object obstructs the throat of the patient.
- the rigid tube is used, the patient is often anesthetized.
- Robotic bronchoscopes provide tremendous advantages in navigation through tubular networks. They can ease use and allow therapies and biopsies to be administered conveniently even during the bronchoscopy stage.
- CT computerized tomography
- 3D three-dimensional
- a medical robotic system comprising a set of one or more processors; and at least one computer-readable memory in communication with the set of processors and having stored thereon computer-executable instructions to cause the set of processors to: receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient, detect a set of one or more points of interest the first image data, identify a set of first locations respectively corresponding to the set of points in the first image data, receive second image data from the image sensor, detect the set of one or more points in the second image data, identify a set of second locations respectively corresponding to the set of points in the second image data, and based on the set of first locations and the set of second locations, detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
- a non-transitory computer readable storage medium having stored thereon instructions that, when executed, cause at least one computing device to: receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient; detect a set of one or more points of interest the first image data; identify a set of first locations respectively corresponding to the set of points in the first image data; receive second image data from the image sensor; detect the set of one or more points in the second image data; identify a set of second locations respectively corresponding to the set of points in the second image data; and based on the set of first locations and the set of second locations, detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
- a method for detecting a change of location of an instrument comprising: receiving first image data from an image sensor located on the instrument, the instrument configured to be driven through a luminal network of a patient; detecting a set of one or more points of interest the first image data; identifying a set of first locations respectively corresponding to the set of points in the first image data; receiving second image data from the image sensor; detecting the set of one or more points in the second image data; identifying a set of second locations respectively corresponding to the set of points in the second image data; and based on the set of first locations and the set of second locations, detecting the change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
- FIG. 1 A shows an example surgical robotic system, according to one embodiment.
- FIGS. 1 B- 1 F show various perspective views of a robotic platform coupled to the surgical robotic system shown in FIG. 1 A , according to one embodiment.
- FIG. 2 shows an example command console for the example surgical robotic system, according to one embodiment.
- FIG. 3 A shows an isometric view of an example independent drive mechanism of the instrument device manipulator (IDM) shown in FIG. 1 A , according to one embodiment.
- IDM instrument device manipulator
- FIG. 3 B shows a conceptual diagram that shows how forces may be measured by a strain gauge of the independent drive mechanism shown in FIG. 3 A , according to one embodiment.
- FIG. 4 A shows a top view of an example endoscope, according to one embodiment.
- FIG. 4 B shows an example endoscope tip of the endoscope shown in FIG. 4 A , according to one embodiment.
- FIG. 5 shows an example schematic setup of an EM tracking system included in a surgical robotic system, according to one embodiment.
- FIGS. 6 A- 6 B show an example anatomical lumen and an example 3D model of the anatomical lumen, according to one embodiment.
- FIG. 7 shows a computer-generated 3D model representing an anatomical space, according to one embodiment.
- FIGS. 8 A- 8 D show example graphs illustrating on-the-fly registration of an EM system to a 3D model of a path through a tubular network, according to one embodiment.
- FIGS. 8 E- 8 F show effect of an example registration of the EM system to a 3D model of a branched tubular network, according to one embodiment.
- FIG. 9 A shows a high-level overview of an example block diagram of a navigation configuration system, according to one embodiment.
- FIG. 9 B shows an example block diagram of the navigation module shown in FIG. 9 A , according to one embodiment.
- FIG. 9 C shows example block diagram of the estimated state data store included in the state estimator, according to one embodiment.
- FIG. 10 A shows an example block diagram of the motion estimation module in accordance with aspects of this disclosure.
- FIG. 10 B shows an example block diagram of the object detection module, according to one example.
- FIG. 10 C shows an example block diagram of the object mapping module, according to one embodiment.
- FIG. 10 D shows an example block diagram of the topological reasoning module, according to one embodiment.
- FIGS. 11 A- 11 B show an example object-to-lumen mapping performed by the object mapping module, according to one embodiment.
- FIG. 12 is a flowchart illustrating an example method operable by a robotic system, or component(s) thereof, for detecting physiological noise in accordance with aspects of this disclosure.
- FIG. 13 A illustrates example image data captured by an image sensor at a first point in time in accordance with aspects of this disclosure.
- FIG. 13 B illustrates another example of image data captured by an image sensor at a second point in time, after the first point in time, in accordance with aspects of this disclosure.
- FIG. 13 C illustrates an example of the change in location of example pixels between the image data frames illustrated in FIGS. 13 A- 13 B in accordance with aspects of this disclosure.
- FIG. 13 D illustrates another example of the change in location of example pixels between the image data frames illustrated in FIGS. 13 A- 13 B in accordance with aspects of this disclosure.
- FIGS. 14 A- 14 B illustrate an example of two image data frames within a sequence of image data frames for which the scale change value may be accumulated in accordance with aspects of this disclosure.
- FIGS. 15 A- 15 B are graphs which illustrate the changes to an accumulated scale change value over a sequence of image data frames in accordance with aspects of this disclosure.
- FIG. 1 A shows an example surgical robotic system 100 , according to one embodiment.
- the surgical robotic system 100 includes a base 101 coupled to one or more robotic arms, e.g., robotic arm 102 .
- the base 101 is communicatively coupled to a command console, which is further described with reference to FIG. 2 in Section II. Command Console.
- the base 101 can be positioned such that the robotic arm 102 has access to perform a surgical procedure on a patient, while a user such as a physician may control the surgical robotic system 100 from the comfort of the command console.
- the base 101 may be coupled to a surgical operating table or bed for supporting the patient. Though not shown in FIG.
- the base 101 may include subsystems such as control electronics, pneumatics, power sources, optical sources, and the like.
- the robotic arm 102 includes multiple arm segments 110 coupled at joints 111 , which provides the robotic arm 102 multiple degrees of freedom, e.g., seven degrees of freedom corresponding to seven arm segments.
- the base 101 may contain a source of power 112 , pneumatic pressure 113 , and control and sensor electronics 114 —including components such as a central processing unit, data bus, control circuitry, and memory—and related actuators such as motors to move the robotic arm 102 .
- the electronics 114 in the base 101 may also process and transmit control signals communicated from the command console.
- the base 101 includes wheels 115 to transport the surgical robotic system 100 .
- Mobility of the surgical robotic system 100 helps accommodate space constraints in a surgical operating room as well as facilitate appropriate positioning and movement of surgical equipment. Further, the mobility allows the robotic arms 102 to be configured such that the robotic arms 102 do not interfere with the patient, physician, anesthesiologist, or any other equipment. During procedures, a user may control the robotic arms 102 using control devices such as the command console.
- the robotic arm 102 includes set up joints that use a combination of brakes and counter-balances to maintain a position of the robotic arm 102 .
- the counter-balances may include gas springs or coil springs.
- the brakes e.g., fail safe brakes, may be include mechanical and/or electrical components.
- the robotic arms 102 may be gravity-assisted passive support type robotic arms.
- Each robotic arm 102 may be coupled to an instrument device manipulator (IDM) 117 using a mechanism changer interface (MCI) 116 .
- the IDM 117 can be removed and replaced with a different type of IDM, for example, a first type of IDM manipulates an endoscope, while a second type of IDM manipulates a laparoscope.
- the MCI 116 includes connectors to transfer pneumatic pressure, electrical power, electrical signals, and optical signals from the robotic arm 102 to the IDM 117 .
- the MCI 116 can be a set screw or base plate connector.
- the IDM 117 manipulates surgical instruments such as the endoscope 118 using techniques including direct drive, harmonic drive, geared drives, belts and pulleys, magnetic drives, and the like.
- the MCI 116 is interchangeable based on the type of IDM 117 and can be customized for a certain type of surgical procedure.
- the robotic 102 arm can include a joint level torque sensing and a wrist at a distal end, such as the KUKA AG® LBR5 robotic arm.
- the endoscope 118 is a tubular and flexible surgical instrument that is inserted into the anatomy of a patient to capture images of the anatomy (e.g., body tissue).
- the endoscope 118 includes one or more imaging devices (e.g., cameras or other types of optical sensors) that capture the images.
- the imaging devices may include one or more optical components such as an optical fiber, fiber array, or lens.
- the optical components move along with the tip of the endoscope 118 such that movement of the tip of the endoscope 118 results in changes to the images captured by the imaging devices.
- the endoscope 118 is further described with reference to FIGS. 3 A- 4 B in Section IV. Endoscope.
- Robotic arms 102 of the surgical robotic system 100 manipulate the endoscope 118 using elongate movement members.
- the elongate movement members may include pull wires, also referred to as pull or push wires, cables, fibers, or flexible shafts.
- the robotic arms 102 actuate multiple pull wires coupled to the endoscope 118 to deflect the tip of the endoscope 118 .
- the pull wires may include both metallic and non-metallic materials such as stainless steel, Kevlar, tungsten, carbon fiber, and the like.
- the endoscope 118 may exhibit nonlinear behavior in response to forces applied by the elongate movement members. The nonlinear behavior may be based on stiffness and compressibility of the endoscope 118 , as well as variability in slack or stiffness between different elongate movement members.
- FIGS. 1 B- 1 F show various perspective views of the surgical robotic system 100 coupled to a robotic platform 150 (or surgical bed), according to various embodiments.
- FIG. 1 B shows a side view of the surgical robotic system 100 with the robotic arms 102 manipulating the endoscopic 118 to insert the endoscopic inside a patient's body, and the patient is lying on the robotic platform 150 .
- FIG. 1 C shows a top view of the surgical robotic system 100 and the robotic platform 150 , and the endoscopic 118 manipulated by the robotic arms is inserted inside the patient's body.
- FIG. 1 D shows a perspective view of the surgical robotic system 100 and the robotic platform 150 , and the endoscopic 118 is controlled to be positioned horizontally parallel with the robotic platform.
- FIG. 1 B shows a side view of the surgical robotic system 100 with the robotic arms 102 manipulating the endoscopic 118 to insert the endoscopic inside a patient's body, and the patient is lying on the robotic platform 150 .
- FIG. 1 C shows a top view of the surgical robotic system 100 and
- FIG. 1 E shows another perspective view of the surgical robotic system 100 and the robotic platform 150 , and the endoscopic 118 is controlled to be positioned relatively perpendicular to the robotic platform.
- the angle between the horizontal surface of the robotic platform 150 and the endoscopic 118 is 75 degree.
- FIG. 1 F shows the perspective view of the surgical robotic system 100 and the robotic platform 150 shown in FIG. 1 E , and in more detail, the angle between the endoscopic 118 and the virtual line 160 connecting one end 180 of the endoscopic and the robotic arm 102 that is positioned relatively farther away from the robotic platform is 90 degree.
- FIG. 2 shows an example command console 200 for the example surgical robotic system 100 , according to one embodiment.
- the command console 200 includes a console base 201 , display modules 202 , e.g., monitors, and control modules, e.g., a keyboard 203 and joystick 204 .
- one or more of the command console 200 functionality may be integrated into a base 101 of the surgical robotic system 100 or another system communicatively coupled to the surgical robotic system 100 .
- a user 205 e.g., a physician, remotely controls the surgical robotic system 100 from an ergonomic position using the command console 200 .
- the console base 201 may include a central processing unit, a memory unit, a data bus, and associated data communication ports that are responsible for interpreting and processing signals such as camera imagery and tracking sensor data, e.g., from the endoscope 118 shown in FIG. 1 . In some embodiments, both the console base 201 and the base 101 perform signal processing for load-balancing.
- the console base 201 may also process commands and instructions provided by the user 205 through the control modules 203 and 204 .
- the control modules may include other devices, for example, computer mice, trackpads, trackballs, control pads, video game controllers, and sensors (e.g., motion sensors or cameras) that capture hand gestures and finger gestures.
- the user 205 can control a surgical instrument such as the endoscope 118 using the command console 200 in a velocity mode or position control mode.
- velocity mode the user 205 directly controls pitch and yaw motion of a distal end of the endoscope 118 based on direct manual control using the control modules.
- movement on the joystick 204 may be mapped to yaw and pitch movement in the distal end of the endoscope 118 .
- the joystick 204 can provide haptic feedback to the user 205 .
- the joystick 204 vibrates to indicate that the endoscope 118 cannot further translate or rotate in a certain direction.
- the command console 200 can also provide visual feedback (e.g., pop-up messages) and/or audio feedback (e.g., beeping) to indicate that the endoscope 118 has reached maximum translation or rotation.
- the command console 200 uses a three-dimensional (3D) map of a patient and pre-determined computer models of the patient to control a surgical instrument, e.g., the endoscope 118 .
- the command console 200 provides control signals to robotic arms 102 of the surgical robotic system 100 to manipulate the endoscope 118 to a target location. Due to the reliance on the 3D map, position control mode requires accurate mapping of the anatomy of the patient.
- users 205 can manually manipulate robotic arms 102 of the surgical robotic system 100 without using the command console 200 .
- the users 205 may move the robotic arms 102 , endoscopes 118 , and other surgical equipment to access a patient.
- the surgical robotic system 100 may rely on force feedback and inertia control from the users 205 to determine appropriate configuration of the robotic arms 102 and equipment.
- the display modules 202 may include electronic monitors, virtual reality viewing devices, e.g., goggles or glasses, and/or other means of display devices.
- the display modules 202 are integrated with the control modules, for example, as a tablet device with a touchscreen.
- the user 205 can both view data and input commands to the surgical robotic system 100 using the integrated display modules 202 and control modules.
- the display modules 202 can display 3D images using a stereoscopic device, e.g., a visor or goggle.
- the 3D images provide an “endo view” (i.e., endoscopic view), which is a computer 3D model illustrating the anatomy of a patient.
- the “endo view” provides a virtual environment of the patient's interior and an expected location of an endoscope 118 inside the patient.
- a user 205 compares the “endo view” model to actual images captured by a camera to help mentally orient and confirm that the endoscope 118 is in the correct—or approximately correct—location within the patient.
- the “endo view” provides information about anatomical structures, e.g., the shape of an intestine or colon of the patient, around the distal end of the endoscope 118 .
- the display modules 202 can simultaneously display the 3D model and computerized tomography (CT) scans of the anatomy the around distal end of the endoscope 118 . Further, the display modules 202 may overlay the already determined navigation paths of the endoscope 118 on the 3D model and scans/images generated based on preoperative model data (e.g., CT scans).
- CT computerized tomography
- a model of the endoscope 118 is displayed with the 3D models to help indicate a status of a surgical procedure.
- the CT scans identify a lesion in the anatomy where a biopsy may be necessary.
- the display modules 202 may show a reference image captured by the endoscope 118 corresponding to the current location of the endoscope 118 .
- the display modules 202 may automatically display different views of the model of the endoscope 118 depending on user settings and a particular surgical procedure. For example, the display modules 202 show an overhead fluoroscopic view of the endoscope 118 during a navigation step as the endoscope 118 approaches an operative region of a patient.
- FIG. 3 A shows an isometric view of an example independent drive mechanism of the IDM 117 shown in FIG. 1 , according to one embodiment.
- the independent drive mechanism can tighten or loosen the pull wires 321 , 322 , 323 , and 324 (e.g., independently from each other) of an endoscope by rotating the output shafts 305 , 306 , 307 , and 308 of the IDM 117 , respectively.
- the output shafts 305 , 306 , 307 , and 308 transfer force down pull wires 321 , 322 , 323 , and 324 , respectively, through angular motion
- the pull wires 321 , 322 , 323 , and 324 transfer force back to the output shafts.
- the IDM 117 and/or the surgical robotic system 100 can measure the transferred force using a sensor, e.g., a strain gauge further described below.
- FIG. 3 B shows a conceptual diagram that shows how forces may be measured by a strain gauge 334 of the independent drive mechanism shown in FIG. 3 A , according to one embodiment.
- a force 331 may direct away from the output shaft 305 coupled to the motor mount 333 of the motor 337 . Accordingly, the force 331 results in horizontal displacement of the motor mount 333 . Further, the strain gauge 334 horizontally coupled to the motor mount 333 experiences strain in the direction of the force 331 . The strain may be measured as a ratio of the horizontal displacement of the tip 335 of strain gauge 334 to the overall horizontal width 336 of the strain gauge 334 .
- the IDM 117 includes additional sensors, e.g., inclinometers or accelerometers, to determine an orientation of the IDM 117 .
- the surgical robotic system 100 can calibrate readings from the strain gauge 334 to account for gravitational load effects. For example, if the IDM 117 is oriented on a horizontal side of the IDM 117 , the weight of certain components of the IDM 117 may cause a strain on the motor mount 333 . Accordingly, without accounting for gravitational load effects, the strain gauge 334 may measure strain that did not result from strain on the output shafts.
- FIG. 4 A shows a top view of an example endoscope 118 , according to one embodiment.
- the endoscope 118 includes a leader 415 tubular component nested or partially nested inside and longitudinally-aligned with a sheath 411 tubular component.
- the sheath 411 includes a proximal sheath section 412 and distal sheath section 413 .
- the leader 415 has a smaller outer diameter than the sheath 411 and includes a proximal leader section 416 and distal leader section 417 .
- the sheath base 414 and the leader base 418 actuate the distal sheath section 413 and the distal leader section 417 , respectively, for example, based on control signals from a user of a surgical robotic system 100 .
- the sheath base 414 and the leader base 418 are, e.g., part of the IDM 117 shown in FIG. 1 .
- Both the sheath base 414 and the leader base 418 include drive mechanisms (e.g., the independent drive mechanism further described with reference to FIG. 3 A-B in Section III. Instrument Device Manipulator) to control pull wires coupled to the sheath 411 and leader 415 .
- the sheath base 414 generates tensile loads on pull wires coupled to the sheath 411 to deflect the distal sheath section 413 .
- the leader base 418 generates tensile loads on pull wires coupled to the leader 415 to deflect the distal leader section 417 .
- Both the sheath base 414 and leader base 418 may also include couplings for the routing of pneumatic pressure, electrical power, electrical signals, or optical signals from IDMs to the sheath 411 and leader 414 , respectively.
- a pull wire may include a steel coil pipe along the length of the pull wire within the sheath 411 or the leader 415 , which transfers axial compression back to the origin of the load, e.g., the sheath base 414 or the leader base 418 , respectively.
- the endoscope 118 can navigate the anatomy of a patient with ease due to the multiple degrees of freedom provided by pull wires coupled to the sheath 411 and the leader 415 .
- pull wires coupled to the sheath 411 and the leader 415 .
- four or more pull wires may be used in either the sheath 411 and/or the leader 415 , providing eight or more degrees of freedom.
- up to three pull wires may be used, providing up to six degrees of freedom.
- the sheath 411 and leader 415 may be rotated up to 360 degrees along a longitudinal axis 406 , providing more degrees of motion.
- the combination of rotational angles and multiple degrees of freedom provides a user of the surgical robotic system 100 with a user friendly and instinctive control of the endoscope 118 .
- FIG. 4 B illustrates an example endoscope tip 430 of the endoscope 118 shown in FIG. 4 A , according to one embodiment.
- the endoscope tip 430 includes an imaging device 431 (e.g., a camera), illumination sources 432 , and ends of EM coils 434 .
- the illumination sources 432 provide light to illuminate an interior portion of an anatomical space. The provided light allows the imaging device 431 to record images of that space, which can then be transmitted to a computer system such as command console 200 for processing as described herein.
- Electromagnetic (EM) coils 434 located on the tip 430 may be used with an EM tracking system to detect the position and orientation of the endoscope tip 430 while it is disposed within an anatomical system.
- EM Electromagnetic
- the coils may be angled to provide sensitivity to EM fields along different axes, giving the ability to measure a full 6 degrees of freedom: three positional and three angular.
- only a single coil may be disposed within the endoscope tip 430 , with its axis oriented along the endoscope shaft of the endoscope 118 ; due to the rotational symmetry of such a system, it is insensitive to roll about its axis, so only 5 degrees of freedom may be detected in such a case.
- the endoscope tip 430 further comprises a working channel 436 through which surgical instruments, such as biopsy needles, may be inserted along the endoscope shaft, allowing access to the area near the endoscope tip.
- V.A Schematic Setup of an EM Tracking System
- FIG. 5 shows an example schematic setup of an EM tracking system 505 included in a surgical robotic system 500 , according to one embodiment.
- multiple robot components e.g., window field generator, reference sensors as described below
- the robotic surgical system 500 includes a surgical bed 511 to hold a patient's body. Beneath the bed 511 is the window field generator (WFG) 512 configured to sequentially activate a set of EM coils (e.g., the EM coils 434 shown in FIG. 4 B ).
- the WFG 512 generates an alternating current (AC) magnetic field over a wide volume; for example, in some cases it may create an AC field in a volume of about 0.5 ⁇ 0.5 ⁇ 0.5 m.
- AC alternating current
- a planar field generator may be attached to a system arm adjacent to the patient and oriented to provide an EM field at an angle.
- Reference sensors 513 may be placed on the patient's body to provide local EM fields to further increase tracking accuracy.
- Each of the reference sensors 513 may be attached by cables 514 to a command module 515 .
- the cables 514 are connected to the command module 515 through interface units 516 which handle communications with their respective devices as well as providing power.
- the interface unit 516 is coupled to a system control unit (SCU) 517 which acts as an overall interface controller for the various entities mentioned above.
- SCU system control unit
- the SCU 517 also drives the field generators (e.g., WFG 512 ), as well as collecting sensor data from the interface units 516 , from which it calculates the position and orientation of sensors within the body.
- the SCU 517 may be coupled to a personal computer (PC) 518 to allow user access and control.
- PC personal computer
- the command module 515 is also connected to the various IDMs 519 coupled to the surgical robotic system 500 as described herein.
- the IDMs 519 are typically coupled to a single surgical robotic system (e.g., the surgical robotic system 500 ) and are used to control and receive data from their respective connected robotic components; for example, robotic endoscope tools or robotic arms.
- the IDMs 519 are coupled to an endoscopic tool (not shown here) of the surgical robotic system 500
- the command module 515 receives data passed from the endoscopic tool.
- the type of received data depends on the corresponding type of instrument attached.
- example received data includes sensor data (e.g., image data, EM data), robot data (e.g., endoscopic and IDM physical motion data), control data, and/or video data.
- sensor data e.g., image data, EM data
- robot data e.g., endoscopic and IDM physical motion data
- control data e.g., endoscopic and IDM physical motion data
- video data e.g., video data.
- a field-programmable gate array (FPGA) 520 may be configured to handle image processing. Comparing data obtained from the various sensors, devices, and field generators allows the SCU 517 to precisely track the movements of different components of the surgical robotic system 500 , and for example, positions and orientations of these components.
- FPGA field-programmable gate array
- the EM tracking system 505 may require a process known as “registration,” where the system finds the geometric transformation that aligns a single object between different coordinate systems. For instance, a specific anatomical site on a patient has two different representations in the 3D model coordinates and in the EM sensor coordinates. To be able to establish consistency and common language between these two different coordinate systems, the EM tracking system 505 needs to find the transformation that links these two representations, i.e., registration. For example, the position of the EM tracker relative to the position of the EM field generator may be mapped to a 3D coordinate system to isolate a location in a corresponding 3D model.
- FIGS. 6 A- 6 B show an example anatomical lumen 600 and an example 3D model 620 of the anatomical lumen, according to one embodiment. More specifically, FIGS. 6 A- 6 B illustrate the relationships of centerline coordinates, diameter measurements and anatomical spaces between the actual anatomical lumen 600 and its 3D model 620 .
- the anatomical lumen 600 is roughly tracked longitudinally by centerline coordinates 601 , 602 , 603 , 604 , 605 , and 606 where each centerline coordinate roughly approximates the center of the tomographic slice of the lumen.
- the centerline coordinates are connected and visualized by a centerline 607 .
- the volume of the lumen can be further visualized by measuring the diameter of the lumen at each centerline coordinate, e.g., coordinates 608 , 609 , 610 , 611 , 612 , and 613 represent the measurements of the lumen 600 corresponding to coordinates 601 , 602 , 603 , 604 , 605 , and 606 .
- coordinates 608 , 609 , 610 , 611 , 612 , and 613 represent the measurements of the lumen 600 corresponding to coordinates 601 , 602 , 603 , 604 , 605 , and 606 .
- FIG. 6 B shows the example 3D model 620 of the anatomical lumen 600 shown in FIG. 6 A , according to one embodiment.
- the anatomical lumen 600 is visualized in 3D space by first locating the centerline coordinates 601 , 602 , 603 , 604 , 605 , and 606 in 3D space based on the centerline 607 .
- the lumen diameter is visualized as a 2D circular space (e.g., the 2D circular space 630 ) with diameters 608 , 609 , 610 , 611 , 612 , and 613 .
- the anatomical lumen 600 is approximated and visualized as the 3D model 620 . More accurate approximations may be determined by increasing the resolution of the centerline coordinates and measurements, i.e., increasing the density of centerline coordinates and measurements for a given lumen or subsection. Centerline coordinates may also include markers to indicate point of interest for the physician, including lesions.
- a pre-operative software package is also used to analyze and derive a navigation path based on the generated 3D model of the anatomical space.
- the software package may derive a shortest navigation path to a single lesion (marked by a centerline coordinate) or to several lesions.
- This navigation path may be presented to the operator intra-operatively either in two-dimensions or three-dimensions depending on the operator's preference.
- the navigation path (or at a portion thereof) may be pre-operatively selected by the operator.
- the path selection may include identification of one or more target locations (also simply referred to as a “target”) within the patient's anatomy.
- FIG. 7 shows a computer-generated 3D model 700 representing an anatomical space, according to one embodiment.
- the 3D model 700 may be generated using a centerline 701 that was obtained by reviewing CT scans that were generated preoperatively.
- computer software may be able to map a navigation path 702 within the tubular network to access an operative site 703 (or other target) within the 3D model 700 .
- the operative site 703 may be linked to an individual centerline coordinate 704 , which allows a computer algorithm to topologically search the centerline coordinates of the 3D model 700 for the optimum path 702 within the tubular network.
- the topological search for the path 702 may be constrained by certain operator selected parameters, such as the location of one or more targets, one or more waypoints, etc.
- the distal end of the endoscopic tool within the patient's anatomy is tracked, and the tracked location of the endoscopic tool within the patient's anatomy is mapped and placed within a computer model, which enhances the navigational capabilities of the tubular network.
- a number of approaches may be employed, either individually or in combination.
- a sensor such as an EM tracker
- EM tracker embedded in the endoscopic tool, measures the variation in the electromagnetic field created by one or more EM transmitters.
- the transmitters or field generators
- the transmitters may be placed close to the patient (e.g., as part of the surgical bed) to create a low intensity magnetic field. This induces small-currents in sensor coils in the EM tracker, which are correlated to the distance and angle between the sensor and the generator.
- the electrical signal may then be digitized by an interface unit (on-chip or PCB) and sent via cables/wiring back to the system cart and then to the command module.
- the data may then be processed to interpret the current data and calculate the precise location and orientation of the sensor relative to the transmitters.
- Multiple sensors may be used at different locations in the endoscopic tool, for instance in leader and sheath in order to calculate the individual positions of those components. Accordingly, based on readings from an artificially-generated EM field, the EM tracker may detect changes in field strength as it moves through the patient's anatomy.
- FIGS. 8 A- 8 D show example graphs 810 - 840 illustrating on-the-fly registration of an EM system to a 3D model of a path through a tubular network, according to one embodiment.
- the navigation configuration system described herein allows for on-the-fly registration of the EM coordinates to the 3D model coordinates without the need for independent registration prior to an endoscopic procedure.
- FIG. 8 A shows that the coordinate systems of the EM tracking system and the 3D model are initially not registered to each other, and the graph 810 in FIG.
- FIG. 8 A shows the registered (or expected) location of an endoscope tip 801 moving along a planned navigation path 802 through a branched tubular network (not shown here), and the registered location of the instrument tip 801 as well as the planned path 802 are derived from the 3D model.
- the actual position of the tip is repeatedly measured by the EM tracking system 505 , resulting in multiple measured location data points 803 based on EM data.
- the data points 803 derived from EM tracking are initially located far from the registered location of the endoscope tip 801 expected from the 3D model, reflecting the lack of registration between the EM coordinates and the 3D model coordinates. There may be several reasons for this, for example, even if the endoscope tip is being moved relatively smoothly through the tubular network, there may still be some visible scatter in the EM measurement, due to breathing movement of the lungs of the patient.
- the points on the 3D model may also be determined and adjusted based on correlation between the 3D model itself, image data received from optical sensors (e.g., cameras) and robot data from robot commands.
- the 3D transformation between these points and collected EM data points will determine the initial registration of the EM coordinate system to the 3D model coordinate system.
- FIG. 8 B shows a graph 820 at a later temporal stage compared with the graph 810 , according to one embodiment. More specifically, the graph 820 shows the expected location of the endoscope tip 801 expected from the 3D model has been moved farther along the preplanned navigation path 802 , as illustrated by the shift from the original expected position of the instrument tip 801 shown in FIG. 8 A along the path to the position shown in FIG. 8 B .
- additional data points 803 have been recorded by the EM tracking system but the registration has not yet been updated based on the newly collected EM data. As a result, the data points 803 in FIG.
- sufficient data e.g., EM data
- a relatively more accurate estimate can be derived from the transform needed to register the EM coordinates to those of the 3D model.
- the determination of sufficient data may be made by threshold criteria such as total data accumulated or number of changes of direction. For example, in a branched tubular network such as a bronchial tube network, it may be judged that sufficient data have been accumulated after arriving at two branch points.
- FIG. 8 C shows a graph 830 shortly after the navigation configuration system has accumulated a sufficient amount of data to estimate the registration transform from EM to 3D model coordinates, according to one embodiment.
- the data points 803 in FIG. 8 C have now shifted from their previous position as shown in FIG. 8 B as a result of the registration transform.
- the data points 803 derived from EM data is now falling along the planned navigation path 802 derived from the 3D model, and each data point among the data points 803 is now reflecting a measurement of the expected position of endoscope tip 801 in the coordinate system of the 3D model.
- the registration transform may be updated to increase accuracy.
- the data used to determine the registration transformation may be a subset of data chosen by a moving window, so that the registration may change over time, which gives the ability to account for changes in the relative coordinates of the EM and 3D models—for example, due to movement of the patient.
- FIG. 8 D shows an example graph 840 in which the expected location of the endoscope tip 801 has reached the end of the planned navigation path 802 , arriving at the target location in the tubular network, according to one embodiment.
- the recorded EM data points 803 is now generally tracks along the planned navigation path 802 , which represents the tracking of the endoscope tip throughout the procedure.
- Each data point reflects a transformed location due to the updated registration of the EM tracking system to the 3D model.
- each of the graphs shown in FIGS. 8 A- 8 D can be shown sequentially on a display visible to a user as the endoscope tip is advanced in the tubular network.
- the processor can be configured with instructions from the navigation configuration system such that the model shown on the display remains substantially fixed when the measured data points are registered to the display by shifting of the measured path shown on the display in order to allow the user to maintain a fixed frame of reference and to remain visually oriented on the model and on the planned path shown on the display.
- FIGS. 8 E- 8 F show the effect of an example registration of the EM system to a 3D model of a branched tubular network, according to one embodiment.
- 3D graphs showing electromagnetic tracking data 852 and a model of a patient's bronchial system 854 are illustrated without (shown in FIG. 8 E ) and with (shown in FIG. 8 F ) a registration transform.
- tracking data 860 have a shape that corresponds to a path through the bronchial system 854 , but that shape is subjected to an arbitrary offset and rotation.
- FIG. 8 F by applying the registration, the tracking data 852 are shifted and rotated, so that they correspond to a path through the bronchial system 854 .
- FIGS. 9 A- 9 C show example block diagrams of a navigation configuration system 900 , according to one embodiment. More specifically, FIG. 9 A shows a high-level overview of an example block diagram of the navigation configuration system 900 , according to one embodiment.
- the navigation configuration system 900 includes multiple input data stores, a navigation module 905 that receives various types of input data from the multiple input data stores, and an output navigation data store 990 that receives output navigation data from the navigation module.
- the block diagram of the navigation configuration system 900 shown in FIG. 9 A is merely one example, and in alternative embodiments not shown, the navigation configuration system 900 can include different and/or addition entities. Likewise, functions performed by various entities of the system 900 may differ according to different embodiments.
- the navigation configuration system 900 may be similar to the navigational system described in U.S. Patent Publication No. 2017/0084027, published on Mar. 23, 2017, the entirety of which is incorporated herein by reference.
- the input data refers to raw data gathered from and/or processed by input devices (e.g., command module, optical sensor, EM sensor, IDM) for generating estimated state information for the endoscope as well as output navigation data.
- the multiple input data stores 910 - 940 include an image data store 910 , an EM data store 920 , a robot data store 930 , and a 3D model data store 940 .
- Each type of the input data stores 910 - 940 stores the name-indicated type of data for access and use by a navigation module 905 .
- Image data may include one or more image frames captured by the imaging device at the instrument tip, as well as information 911 such as frame rates or timestamps that allow a determination of the time elapsed between pairs of frames.
- Robot data may include data related to physical movement of the medical instrument or part of the medical instrument (e.g., the instrument tip or sheath) within the tubular network.
- Example robot data includes command data instructing the instrument tip to reach a specific anatomical site and/or change its orientation (e.g., with a specific pitch, roll, yaw, insertion, and retraction for one or both of a leader and a sheath) within the tubular network, insertion data representing insertion movement of the part of the medical instrument (e.g., the instrument tip or sheath), IDM data, and mechanical data representing mechanical movement of an elongate member of the medical instrument, for example motion of one or more pull wires, tendons or shafts of the endoscope that drive the actual movement of the medial instrument within the tubular network.
- EM data may be collected by EM sensors and/or the EM tracking system as described above.
- 3D model data may be derived from 2D CT scans as described above.
- the output navigation data store 990 receives and stores output navigation data provided by the navigation module 905 .
- Output navigation data indicates information to assist in directing the medical instrument through the tubular network to arrive at a particular destination within the tubular network, and is based on estimated state information for the medical instrument at each instant time, the estimated state information including the location and orientation of the medical instrument within the tubular network.
- the output navigation data indicating updates of movement and location/orientation information of the medical instrument is provided in real time, which better assists its navigation through the tubular network.
- the navigation module 905 locates (or determines) the estimated state of the medical instrument within a tubular network. As shown in FIG. 9 A , the navigation module 905 further includes various algorithm modules, such as an EM-based algorithm module 950 , an image-based algorithm module 960 , and a robot-based algorithm module 970 , that each may consume mainly certain types of input data and contribute a different type of data to a state estimator 980 . As illustrated in FIG. 9 A , the different kinds of data output by these modules, labeled EM-based data, the image-based data, and the robot-based data, may be generally referred to as “intermediate data” for sake of explanation. The detailed composition of each algorithm module and of the state estimator 980 is more fully described below.
- FIG. 9 B shows an example block diagram of the navigation module 905 shown in FIG. 9 A , according to one embodiment.
- the navigation module 905 further includes a state estimator 980 as well as multiple algorithm modules that employ different algorithms for navigating through a tubular network.
- the state estimator 980 is described first, followed by the description of the various modules that exchange data with the state estimator 980 .
- the state estimator 980 included in the navigation module 905 receives various intermediate data and provides the estimated state of the instrument tip as a function of time, where the estimated state indicates the estimated location and orientation information of the instrument tip within the tubular network.
- the estimated state data are stored in the estimated data store 985 that is included in the state estimator 980 .
- FIG. 9 C shows an example block diagram of the estimated state data store 985 included in the state estimator 980 , according to one embodiment.
- the estimated state data store 985 may include a bifurcation data store 1086 , a position data store 1087 , a depth data store 1088 , and an orientation data store 1089 , however this particular breakdown of data storage is merely one example, and in alternative embodiments not shown, different and/or additional data stores can be included in the estimated state data store 985 .
- bifurcation data refers to the location of the medical instrument with respect to the set of branches (e.g., bifurcation, trifurcation or a division into more than three branches) within the tubular network.
- the bifurcation data can be set of branch choices elected by the instrument as it traverses through the tubular network, based on a larger set of available branches as provided, for example, by the 3D model which maps the entirety of the tubular network.
- the bifurcation data can further include information in front of the location of the instrument tip, such as branches (bifurcations) that the instrument tip is near but has not yet traversed through, but which may have been detected, for example, based on the tip's current position information relative to the 3D model, or based on images captured of the upcoming bifurcations.
- branches branches
- Position data indicates three-dimensional position of some part of the medical instrument within the tubular network or some part of the tubular network itself. Position data can be in the form of absolute locations or relative locations relative to, for example, the 3D model of the tubular network. As one example, position data can include an indication of the position of the location of the instrument being within a specific branch. The identification of the specific branch may also be stored as a segment identification (ID) which uniquely identifies the specific segment of the model in which the instrument tip is located.
- ID segment identification
- Depth data indicates depth information of the instrument tip within the tubular network.
- Example depth data includes the total insertion (absolute) depth of the medical instrument into the patient as well as the (relative) depth within an identified branch (e.g., the segment identified by the position data store 1087 ).
- Depth data may be determined based on position data regarding both the tubular network and medical instrument.
- Orientation data indicates orientation information of the instrument tip, and may include overall roll, pitch, and yaw in relation to the 3D model as well as pitch, roll, raw within an identified branch.
- the state estimator 980 provides the estimated state data back to the algorithm modules for generating more accurate intermediate data, which the state estimator uses to generate improved and/or updated estimated states, and so on forming a feedback loop.
- the EM-based algorithm module 950 receives prior EM-based estimated state data, also referred to as data associated with timestamp “t- 1 .”
- the state estimator 980 uses this data to generate “estimated state data (prior)” that is associated with timestamp “t- 1 .”
- the state estimator 980 then provides the data back to the EM-based algorithm module.
- the “estimated state data (prior)” may be based on a combination of different types of intermediate data (e.g., robotic data, image data) that is associated with timestamp “t- 1 ” as generated and received from different algorithm modules.
- the EM-based algorithm module 950 runs its algorithms using the estimated state data (prior) to output to the state estimator 980 improved and updated EM-based estimated state data, which is represented by “EM-based estimated state data (current)” here and associated with timestamp t. This process continues to repeat for future timestamps as well.
- the state estimator 980 may use several different kinds of intermediate data to arrive at its estimates of the state of the medical instrument within the tubular network
- the state estimator 980 is configured to account for the various different kinds of errors and uncertainty in both measurement and analysis that each type of underlying data (robotic, EM, image) and each type of algorithm module might create or carry through into the intermediate data used for consideration in determining the estimated state.
- robot EM
- image a type of underlying data
- each type of algorithm module might create or carry through into the intermediate data used for consideration in determining the estimated state.
- the “probability” of the “probability distribution”, as used herein, refers to a likelihood of an estimation of a possible location and/or orientation of the medical instrument being correct. For example, different probabilities may be calculated by one of the algorithm modules indicating the relative likelihood that the medical instrument is in one of several different possible branches within the tubular network.
- the type of probability distribution e.g., discrete distribution or continuous distribution
- is chosen to match features of an estimated state e.g., type of the estimated state, for example continuous position information vs. discrete branch choice).
- estimated states for identifying which segment the medical instrument is in for a trifurcation may be represented by a discrete probability distribution, and may include three discrete values of 20%, 30% and 50% representing chance as being in the location inside each of the three branches as determined by one of the algorithm modules.
- the estimated state may include a roll angle of the medical instrument of 40 ⁇ 5 degrees and a segment depth of the instrument tip within a branch may be is 4 ⁇ 1 mm, each represented by a Gaussian distribution which is a type of continuous probability distribution. Different methods or modalities can be used to generate the probabilities, which will vary by algorithm module as more fully described below with reference to later figures.
- the “confidence value,” as used herein, reflects a measure of confidence in the estimation of the state provided by one of the algorithms based one or more factors.
- factors such as distortion to EM Field, inaccuracy in EM registration, shift or movement of the patient, and respiration of the patient may affect the confidence in estimation of the state.
- the confidence value in estimation of the state provided by the EM-based algorithms may depend on the particular respiration cycle of the patient, movement of the patient or the EM field generators, and the location within the anatomy where the instrument tip locates.
- examples factors that may affect the confidence value in estimation of the state include illumination condition for the location within the anatomy where the images are captured, presence of fluid, tissue, or other obstructions against or in front of the optical sensor capturing the images, respiration of the patient, condition of the tubular network of the patient itself (e.g., lung) such as the general fluid inside the tubular network and occlusion of the tubular network, and specific operating techniques used in, e.g., navigating or image capturing.
- one factor may be that a particular algorithm has differing levels of accuracy at different depths in a patient's lungs, such that relatively close to the airway opening, a particular algorithm may have a high confidence in its estimations of medical instrument location and orientation, but the further into the bottom of the lung the medical instrument travels that confidence value may drop.
- the confidence value is based on one or more systemic factors relating to the process by which a result is determined, whereas probability is a relative measure that arises when trying to determine the correct result from multiple possibilities with a single algorithm based on underlying data.
- a mathematical equation for calculating results of an estimated state represented by a discrete probability distribution (e.g., branch/segment identification for a trifurcation with three values of an estimated state involved) can be as follows:
- a user is trying to identify segment where an instrument tip is located in a certain trifurcation within a central airway (the predicted region) of the tubular network, and three algorithms modules are used including EM-based algorithm, image-based algorithm, and robot-based algorithm.
- EM-based algorithm the probability distribution corresponding to the EM-based algorithm may be 20% in the first branch, 30% in the second branch, and 50% in the third (last) branch, and the confidence value applied to this EM-based algorithm and the central airway is 80%.
- a probability distribution corresponding to the image-based algorithm may be 40%, 20%, 40% for the first, second, and third branch, and the confidence value applied to this image-based algorithm is 30%; while a probability distribution corresponding to the robot-based algorithm may be 10%, 60%, 30% for the first, second, and third branch, and the confidence value applied to this image-based algorithm is 20%.
- the difference of confidence values applied to the EM-based algorithm and the image-based algorithm indicates that the EM-based algorithm may be a better choice for segment identification in the central airway, compared with the image-based algorithm.
- An example mathematical calculation of a final estimated state can be:
- the output estimated state for the instrument tip can be the result values (e.g., the resulting 30%, 42% and 58%), or derivative value from these result values such as the determination that the instrument tip is in the third branch.
- the estimated state may be represented in a number of different ways.
- the estimated state may further include an absolute depth from airway to location of the tip of the instrument, as well as a set of data representing the set of branches traversed by the instrument within the tubular network, the set being a subset of the entire set of branches provided by the 3D model of the patient's lungs, for example.
- the application of probability distribution and confidence value on estimated states allows improved accuracy of estimation of location and/or orientation of the instrument tip within the tubular network.
- the algorithm modules include an EM-based algorithm module 950 , an image-based algorithm module 960 , and a robot-based algorithm module 970 .
- the algorithm modules shown in FIG. 9 B is merely one example, and in alternative embodiments, different and/additional algorithm modules involving different and/or additional navigation algorithms can also be included in the navigation module 905 .
- the image-based algorithm module 960 uses image data to determine the estimated state of the instrument within the tubular network.
- the image-based algorithm module 960 further includes one or more different types of image-based algorithm modules that employ different image-based algorithms. As shown in FIG. 9 B , one example including an object-based algorithm module 962 is shown. In alternative embodiments not shown, other types of image-based algorithms may be employed and corresponding algorithm modules may be included in the image-based algorithm module 960 .
- the image-based algorithm module 960 may also detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument and provide an indication of the detected change in location to the state estimator 980 . Further detail regarding aspects of the detection of the change in location of the instrument will be provided below in connection with FIG. 11 .
- the object-based algorithm module 962 detects and analyzes objects present in the field of view of the image data, such as branch openings or particles, to determine estimated state. In one embodiment, it includes an object detection module 963 , and object mapping module 964 , a topological reasoning module 965 , and a motion estimation module 966 . In some embodiments, it may or may not be necessary to apply the different modules 963 , 964 , 965 and 966 in a fixed sequential order, and when actually executing a process of object-based algorithm described by the object-based algorithm module 962 , the order of employing each module within the module 962 is a different order than shown in FIG. 9 B .
- the motion estimation module 963 receives as inputs image data from the image data store 910 , estimated state data (prior) (specifically bifurcation data), from the estimated state data store 985 as well as the 3D model data from the 3D model data store 940 . Based on the received image data, the motion estimation module 963 measures a movement of the medical instrument between multiple image frames based on the received image data. Example techniques used include optical flow and image registration techniques, among others. This measurement determines a differential movement, such as forward-backward motion or roll motion, of the instrument tip in its own local frame of reference. This movement can be combined with the prior estimated state input to calculate a new estimated state.
- a forward (or backward) movement can translate into an increase (or decrease) in depth relative to a prior estimated state.
- a differential roll translates into a change in roll angle relative to a prior estimated state.
- this movement measurement may include an identification of an estimated new branch that the instrument tip is estimated to be entering or have entered. For example, if the bifurcation data indicates that the endoscope tip is at a branch point, pitch and yaw movements can be measured to determine changes in pointing angle, and the new estimated angle can be compared with the expected angles of different branches in the 3D model of the tubular network. A determination can then be made of which branch the endoscope is facing towards when it is moved into a new branch. Estimated state data reflecting each of these estimates of new position, orientation, and/or branch entry are output to the state estimator 980 .
- FIG. 10 B shows an example block diagram of the object detection module 964 , according to one example.
- the object detection module 964 receives as inputs image data (e.g., image frames), and outputs object data to an object data store 1063 as well as estimated state data to the estimated state data store 985 .
- Object data indicates information about what objects were identified, as well as positions, orientations, and sizes of objects represented as probabilities.
- the object detection module 964 detects, within an image, one or more objects and one or more points of interest of the object(s) that may indicate branch points in a tubular network, and then determines their position, size, and orientation.
- the objects detected by the object detection module 964 may also be referred to as “points of interest,” which may include, for example, one or more identifiable pixels within an image.
- the points of interest may comprise a set of one or more pixels which can be detected over a sequence of one or more images.
- the detected object may comprise one of more points of interest (e.g., image features) detected using image processing techniques of the related art, such as speeded up robust features (SURF) and scale-invariant feature transform (SIFT).
- SURF speeded up robust features
- SIFT scale-invariant feature transform
- objects may be calculated or represented in the object detection module 964 as being two-dimensional shapes, such as circles/ovals/ellipses for detected branch points. This corresponds to the fact that the image data used to capture the objects are images from the camera on the instrument tip pointed usually along an axis substantially parallel to the direction of the segment in which the instrument is located.
- objects such as branches in the tubular network appear as simple shapes such as ellipses in the images.
- each branch will typically appear as a dark, approximately elliptical region, and these regions may be detected automatically by a processor, using region-detection algorithms such as maximally stable extremal regions (MSER) as objects.
- MSER maximally stable extremal regions
- regions may then be fit to define an object (e.g., ellipse), with appropriate free parameters such as ellipse center, major and minor axes, and angle within the image.
- object e.g., ellipse
- the roll measurement and the identified matching between model lumens and lumens in the image are also output to the state estimator 980 , as well as topological reasoning module 966 .
- An example of identified objects superimposed on an image of a bronchial network, along with a link joining their centers, is described with reference to FIGS. 11 A- 11 B .
- an “airway” can also be identified as an object present in the image data.
- the object detection module 964 may use light reflective intensity combined with other techniques to identify airways.
- the object detection module 964 may further track detected objects or points of interest across a set of sequential image frames.
- the object tracking may be used to detect which branch has been entered from among a set of possible branches in the tubular network.
- the tracking of objects may be used to detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument as described in greater detail below. Tracking the relative positions of the objects within the image frames may also be used to determine a local, absolute measurement of roll angle within a branched network.
- FIG. 10 C shows an example block diagram of the object mapping module 965 , according to one embodiment.
- the object mapping module 965 receives as inputs 3D model data from the 3D model data store 940 , object data (e.g., detected objects such as shapes representing possible branches in the tubular network) from the object data store 1063 , and estimated state data (prior) from the estimated state data store 985 .
- object data e.g., detected objects such as shapes representing possible branches in the tubular network
- estimated state data prior
- the object mapping module 965 Based on the received input data, the object mapping module 965 outputs object mapping data to an object mapping data store 1065 as well as image-based estimated state data (current) to the estimated state data store 985 .
- the object mapping data indicates mapping information between physical branches (lumens) shown in image data (based on the detected objects) and virtual branch information generated by 3D model.
- the estimated state data (current) generated by module 965 includes identification of each branch of the tubular network visible within the image as well as an estimate of the roll of the endoscope tip relative to the 3D model.
- the estimated state data (current) can be represented as a probability distribution.
- the identification of the visible lumens may include coordinates in x and y of each identified lumen center within the image, for example based on object sizes correlated with the 3D model virtual image data, as well as an association of each identified lumen location with a particular branch of the tubular network.
- the virtual images of the tubular network may be pre-computed to speed up processing.
- the tubular network may be represented by a structure such as a tree diagram of lumen midlines, with each such midline describing a 3D path, so that an expected position of local branch centers as seen from any arbitrary perspective may be compared to the identified actual locations of branch centers based on EM data and/or robot data.
- FIGS. 11 A- 11 B show an example object-to-lumen mapping performed by the object mapping module 965 , according to one embodiment. More specifically, FIG. 11 A shows two example identified objects 1101 and 1102 superimposed on an image of a bronchial network 1105 along with a link 1103 connecting centers of the two objects, according to one embodiment. In the illustrated example, the identified objects 1101 and 1102 are ellipse-shaped.
- FIG. 11 B shows a matching between airway lumens in an actual image 1110 of a real bronchial network and a corresponding virtual image 1120 from a 3D model of that same network, according to one embodiment.
- ellipses are identified corresponding to two different branches, located with identified centers 1111 and 1112 , which, in one embodiment, indicates centerline coordinates of the branches as described above in FIGS. 6 A- 6 B .
- the 3D model virtual image 1120 is a simulated representation of the real bronchial network shown in the actual image 1110 , and the estimated centers 1121 and 1122 of the endoscope tip as determined by the state estimator 980 are shown corresponding to the positions of the identified centers 1111 and 1112 .
- the 3D model image 1120 may be rotated or translated to increase the closeness of fit between actual image 1110 and virtual image 1120 , and the amount of roll needed for the rotation or translation can be output as a correction to the current estimated state (e.g., roll of the instrument tip).
- the probability applied to a possible estimated state as generated by the object mapping module 965 is based on the closeness of fit between the identified centers 1111 and 1112 detected in the actual image 1110 and estimated centers 1121 and 1121 in the 3D model image 1120 , and as one example, the probability of being in the lumen with identified center 1112 drops as the distance between the estimated center 1122 and identified center 1112 increases.
- FIG. 10 D shows an example block diagram of the topological reasoning module 966 , according to one embodiment.
- the topological reasoning module 966 receives as input image data from the 3D model data from the 3D model data store 940 , object mapping data from the object mapping data store 1065 , and estimated state data (prior) from the estimated state data store 985 .
- the topological reasoning module 966 determines which branch the endoscope tip is facing towards, thereby generating a prediction of which branch will be entered if the endoscope is moved forward. As above, the determination may be represented as a probability distribution. In one embodiment, when the instrument tip is moving forward, the topological reasoning module 966 determines that a new branch of the tubular network has been entered and identifies which branch the tip has moved into. The determination of which branch is being faced and which segment is entered may be made, for example, by comparing the relative sizes and locations of different identified objects (e.g., ellipses). As one example, as a particular lumen branch is entered, a corresponding detected object will grow larger in successive image frames, and will also become more centered in those frames.
- identified objects e.g., ellipses
- the topological reasoning module 966 assigns an increasingly large probability to a corresponding estimated state as the endoscope tip moves towards the lumen associated with that object. Other branches are assigned correspondingly lower probabilities, until finally their object shapes disappear from images entirely. In one embodiment, the probability of the medical instrument being in those branches depends only on the probability that the branches were misidentified by the object mapping module 964 .
- the output of the topological reasoning module 966 is image-based estimated state data representing estimated probabilities of being in each of a set of possible branches within the branched network.
- Pulmonologists can prevent intra-operative trauma by basing their decisions and actions on the respiratory cycle of the patient.
- One example of such an action is insertion of a biopsy tool to collect tissue samples, for example via bronchoscopy.
- the airways may be narrow, and the circumference of the airways changes depending on the respiratory phase of the lung.
- the diameter of an airway expands as a patient inhales in the inspiration phase of the respiratory cycles and constricts as the patient exhales during the expiration phase of the cycle.
- a pulmonologist can observe the patient to determine whether they are in the inspiration phase or the expiration phase in order to decide whether a particular tool or endoscope of fixed diameter can enter the airway.
- An airway can close around a tool during expiration without causing trauma, however forcing a tool through a constricted airway during the expiration phase can cause critical trauma, for example by puncturing a blood vessel.
- Some embodiments of the disclosed luminal network navigation systems and techniques relate to incorporating respiratory frequency and/or magnitude into a navigation framework to implement patient safety measures (e.g., instrument control techniques, user interface alerts, notifications, and the like).
- patient safety measures e.g., instrument control techniques, user interface alerts, notifications, and the like.
- a patient's respiratory cycle may also affect the accuracy of the detection of the position and/or orientation of an instrument inserted into the patient's airways.
- some embodiments of the disclosed bronchoscopy navigation systems and techniques relate to identifying, and/or compensating for, motion caused by patient respiration in order to provide a more accurate identification of the position of an instrument within patient airways.
- an instrument positioned within patient airways can be provided with an EM sensor.
- the navigation system can filter instrument position information from the EM sensor to remove signal noise due to cyclic motion of the respiratory passages caused by respiration.
- a frequency of the cyclic respiratory motion can be obtained from data from one or more additional sensors.
- inspiration and expiration cycles can be determined based on data from additional EM sensor(s), accelerometer(s), and/or acoustic respiratory sensor(s) placed on the body of the patient in one example.
- the frequency can be obtained from other types of sensors or systems, for example respiratory cycle information from a ventilator used to control patient breathing, or respiratory cycle information extracted from automated analysis of images received from an optical sensor positioned to observe the patient.
- the filtering of the patient's respiration from the position information received from the EM sensor may not be sufficient to determine a sufficiently accurate estimate of the position of the instrument.
- the EM sensors may detect the motion due to respiration in a transverse direction. That is, the EM sensor may track the overall expansion and contraction of the patient's airway via the movement of the EM sensors placed on the body of the patient.
- the patient's respiration may also have another effect on the location of the instrument. That is, the length of the path traversed by the instrument within the luminal network may expand and contract along with the respiratory cycle. Since the length of the instrument may not appreciably change during the procedure, the relative position of the instrument with respect to the luminal network may change as the overall length of luminal network defined by the path taken by the instrument in the luminal network expands and contracts. From the reference point of the distal end of the instrument, this may appear as though the instrument is being advanced and retraced within the luminal network even though the instrument is not being actively driven.
- the instrument may be substantially stationary with respect to the reference point of the platform even while from the reference point of the distal end of the instrument, the instrument is being advanced and retraced.
- the location of the instrument determined based on the EM sensor may indicate that the instrument is substantially stationary, the location of the instrument with respect to the reference frame of the luminal network may be changing in accordance with the patient's respiratory cycle.
- certain aspects of this disclosure may relate to the detection of movement of the instrument with respect to the reference frame of the luminal network (e.g., movement of the luminal network around the instrument) due to a patient's respiration (or other physiological motion).
- the robotic system may provide a user interface alert to indicate that there may be a certain amount of uncompensated error in the displayed location of the instrument.
- FIG. 12 is a flowchart illustrating an example method operable by a robotic system, or component(s) thereof, for detecting physiological noise in accordance with aspects of this disclosure.
- the steps of method 1200 illustrated in FIG. 12 may be performed by processor(s) and/or other component(s) of a medical robotic system (e.g., surgical robotic system 500 ) or associated system(s) (e.g., the image-based algorithm module 960 of the navigation configuration system 900 ).
- a medical robotic system e.g., surgical robotic system 500
- associated system(s) e.g., the image-based algorithm module 960 of the navigation configuration system 900
- the method 1200 is described as performed by the navigation configuration system, also referred to simply as the “system” in connection with the description of the method 1200 .
- the method 1200 begins at block 1201 .
- the system may receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient.
- the instrument may comprise a bronchoscope configured to be driven through a patient's airways.
- the system may be configured to detect respiratory motion of the instrument based at least in part on the images received from the image sensor.
- the system may detect a set of one or more points of interest the first image data.
- the points of interest may be any distinguishable data across multiple image data, such as, for example, one or more identifiable pixels within the image or one or more objects detected in the image data.
- the points of interest may comprise a set of one or more pixels which can be detected over a sequence of one or more images.
- the detected object may comprise one of more distinguishable objects detected using image processing techniques of the related art, such as SURF and SIFT.
- image processing techniques of the related art such as SURF and SIFT.
- any technique which can reliably detect and track one or more pixels through a series of images can be used to detect the points of interest which can be used in the image processing techniques described herein.
- the system may identify a set of first locations respectively corresponding to the set of points in the first image data.
- the set of locations may correspond to the row and/or column values of the pixels within the image.
- the first set of locations may comprise the X- and Y-coordinates for each of the pixels in the set of points.
- the system may receive second image data from the image sensor.
- the second image may be an image received from the image sensor at a point in time occurring after the time at which the first image was captured by the image sensor.
- the system may detect the set of one or more points in the second image data.
- the set of points detected in the second image may correspond to the set of points detected in the first image. The detection of the same set of points between multiple images will be described in greater detail below in connection with FIGS. 13 A- 13 C .
- the system may identify a set of second locations respectively corresponding to the set of points in the second image data.
- the set of locations of the points of interest in the second image may be different from the set of locations of the points of interest in the first image.
- an object appearing in the images captured by the image sensor may appear as though it is approaching the image sensor.
- the system may be able to estimate motion of the instrument with respect to the luminal network.
- the system may, based on the set of first locations and the set of second locations, detect a change of location of the luminal network around the instrument caused by movement of the luminal network relative to the instrument. As described above, movement of the location of the tracked points of interest may be indicative of movement of the luminal network with respect to the instrument. Specific embodiments related to the detection of the change of location caused by movement of the luminal network will be described below in connection with FIGS. 14 A- 15 B .
- the method 1200 ends at block 1240 .
- FIG. 13 A illustrates example image data captured by an image sensor at a first point in time in accordance with aspects of this disclosure.
- FIG. 13 B illustrates another example of image data captured by an image sensor at a second point in time, after the first point in time, in accordance with aspects of this disclosure.
- the first image data and the second image data may be successive images in a series of image data frames captured by the image sensor or may be separated in time with at least one additional image data frame interposed therebetween.
- FIG. 13 C illustrates an example of the change in location of example pixels between the image data frames illustrated in FIGS. 13 A- 13 B in accordance with aspects of this disclosure.
- FIG. 13 D illustrates another example of the change in location of example pixels between the image data frames illustrated in FIGS. 13 A- 13 B in accordance with aspects of this disclosure.
- the image data frames illustrated in FIGS. 13 A- 13 B are simplified to show certain aspects of the detected image data which may be involved in the tracking of the locations of points of interest between a series of image data frames.
- the image data captured by an image sensor of an instrument may include an array of pixels having a greater or lesser number of pixels than illustrated.
- the image sensor may be configured to capture 200 ⁇ 200 pixel image data frames in certain implementations.
- FIG. 13 A illustrates first image data 1300 A including two points of interest 1310 A and 1320 A.
- the limited number of points of interest 1310 A and 1320 A is simplified merely for descriptive purposes and, in other embodiments, a larger number of points of interest 1310 A and 1320 A may be detected and tracked in the first image data 1300 A.
- the points of interest 1310 A and 1320 A may correspond to individual pixels within the first image data 1300 A.
- the points of interest 1310 A and 1320 A may correspond to objects identified by the object detection module 964 or points of interest detected using image processing techniques such as SURF and/or SIFT.
- the system may detect the same points of interest 1310 B and 1320 B which were identified in the first image data 1300 A. However, the points of interest 1310 B and 1320 B may have moved to new locations within the second image data 1300 B in the time elapsed between the capturing of the first and second image data 1300 A and 1300 B. The movement of the points of interest 1310 B and 1320 B from the first image data 1300 A to their respective locations in the second image data 1300 B may be based on the relative movement of the corresponding portions of the luminal network with respect to the location of the image sensor.
- the system may be able to infer that movement of the points of interest 1310 B and 1320 B, and thus instrument within the luminal network, is due physiological noise.
- the physiological noise may correspond to the length of the luminal network expanding and contracting along the path taken by the instrument due to respiration of the patient.
- the change in the diameter of the airway due to respiration may also be tracked by changes in the locations of the points of interest. Examples of physiological noise which may be detected by the image methodologies disclosed herein include respiration of the patient and a heart rate of the patient.
- the locations of the points of interest in FIGS. 13 A- 14 B may include information 911 (e.g., see FIG. 10 A ) the 2D locations of the points within the first image data and the second image data.
- the locations of the points of interest may include the X- and Y-coordinates for each of the points.
- the system may track information 911 such as the location of the points in 3D space (not illustrated) based on the image data 1300 A and 1300 B.
- the system may extract depth information from the each of the image data 1300 A and 1300 B and represent the location of the points in 3D information 911 indicative of the respective locations of the points.
- FIG. 13 C illustrates the locations of the points of interest at each of the first point in time and the second point in time overlaid on the same image data frame.
- a first point of interest 1310 A and 1310 B moved from the first image data 1300 A to different location in the second image data 1300 B.
- the movement of the first point of interest is illustrated by the vector 1315 .
- a second point of interest 1320 A and 1320 B has moved between the first and second image data 1300 A and 1300 B as illustrated by the vector 1325 .
- the system may track a set of points of interest over a series of image data frames received from an image sensor positions on the instrument.
- the system may determine a “scale change” between two successive image data frames in the series.
- FIG. 13 D illustrates another example of the locations of the points of interest at each of the first point in time and the second point in time overlaid on the same image data to illustrate the relative distances between the points of interest.
- a first point of interest 1310 A and 1310 B moved from the first image data 1300 A to different location in the second image data 1300 B.
- the system may determine a determine a first distance 1330 between the first point 1310 A and the second point 1320 A in the first image data based on the locations of the first point 1310 A and the second point 1320 A in the first image data 1300 A.
- the system may also determine a second distance 1335 between the first point 1310 B and the second point 1320 B in the second image data 1300 B based on the locations of the first point 1310 B and the second point 1320 B in the first image data 1300 B.
- the first and second distances 1330 and 1335 distance may be determined by the Euclidean distance between the respective points.
- the system may use the first distance 1330 and the second distance 1335 to detect the change of location of the instrument within the luminal network. For example, in one embodiment, the system may determine a scale change estimate based on the first distance 1330 and the second distance 1335 . In one implementation, the scale change estimate may be based on the difference between the first distance 1330 and the second distance 1335 .
- the system may track a set of at least three points of interest over the series of image data frames.
- the set of points may be considered a “sparse” set of points.
- the number of points in the set of points of interest tracked by the system may be a “dense” set of points, where the number of tracked points is equal to the number of pixels in the image data.
- the system may group the points in the set of points a plurality of pairs of points. This may include each combination of pairs of points for the entire set of points or may include a subset of the possible pairs of points for the set of points.
- the system may determine a scale change value between the two image data frames based on the scale estimates determined for the pairs of points.
- the scale change value may be representative of the scale change between the two image data frames based on all or a subset of the tracked pairs of points.
- the system may determine the scale change value as a median value of the scale change estimates.
- the system may determine the scale change value as an average value of the scale change estimates.
- the system may accumulate a scale change value over a sequence of image data frames, and there by track the scale change over more than two image data frames.
- the system may accumulate the scale change value my multiplying the scale change values between successive pairs of image data frames in the sequence of image data frames.
- FIGS. 14 A- 14 B illustrate an example of two image data frames within a sequence of image data frames for which the scale change value may be accumulated in accordance with aspects of this disclosure.
- FIGS. 15 A- 15 B are graphs which illustrate the changes to an accumulated scale change value over a sequence of image data frames in accordance with aspects of this disclosure.
- FIGS. 14 A- 15 B a sequence of image data is illustrated over a numbered sequence of image data frames, where FIG. 15 A includes image data from frame # 930 to frame # 1125 and FIG. 15 B includes image data from frame # 965 to frame # 1155 .
- FIG. 14 A includes image data 1405 from frame # 1125 while FIG. 14 B includes image data 1410 from frame # 1155 .
- FIGS. 15 A- 15 B illustrates cumulative scale values determined in accordance with aspects of this disclosure.
- the values at each frame in the graphs may be calculated by multiplying a currently determined scale change value between two image data frames with the accumulated scale change value determined for the previous frame.
- the cumulative scale change values are period when periodic physiological noise is affecting the position of the image sensor (and thus the distal end of the instrument).
- the system may track cumulative changes to the scale change value in the sequence of image data received from the image sensor over a first time period and determine the frequency of the physiological noise based on the cumulative scale change values over a period of time.
- the system may transform the tracked scale change value into a frequency domain (e.g., using a Fourier or other transform).
- the system may further identify at least one harmonic in the tracked scale change value in frequency domain.
- the system may identify the first harmonic in the tracked scale change value in frequency domain as an estimated frequency of the physiological noise.
- the frequency determined from the cumulative scale change values may be utilized as an estimate of the frequency of physiological noise.
- physiological noise may not always have a large enough effect on the location of the instrument with respect to the luminal network that the physiological noise will introduce error in the localization of the instrument (e.g., as determined by the navigation configuration system 900 ).
- the system may compare the estimated frequency of the physiological noise to a separately estimated frequency of the physiological noise.
- the system may determine a first physiological movement frequency of the patient based on a sequence of image data frames received from the image sensor.
- the system may further determine a second physiological movement frequency of the patient based on the data received from one or more location sensors (e.g., an EM sensor, a shape-sensing fiber, robot command data, and a radiation-based image sensors).
- location sensors e.g., an EM sensor, a shape-sensing fiber, robot command data, and a radiation-based image sensors.
- the system may then determine whether the difference between the first physiological movement frequency based on the sequence of image data and the second physiological movement frequency based on the location sensor data is less than a threshold difference. When the difference between the first and second physiological movement frequencies is less than the threshold difference, the system may determine that the frequency of the scale changes in the sequence of image data frames is due to a physiological noise source. In certain embodiments, the system may provide an indication of the detected change of location of the instrument within the luminal network to a display in response to determining that the frequency of the scale changes in the sequence of image data frames is due to a physiological noise source.
- the system may not have sufficient confidence to determine that the movement in the luminal network with respect to the instrument will affect the accuracy of the in the localization of the instrument (e.g., as determined by the navigation configuration system 900 ).
- the system may infer that the location of the instrument sufficiently stable with respect to the luminal network so as to not introduce errors into the localization of the instrument.
- the system may identify pixels 1310 B and 1320 B in image data frame 1300 B by tracking a change in the location of pixels 1310 A and 1320 A from frame 1300 A.
- the system may identify different pixels from the original pixels 1310 A and 1320 A.
- the identified pixels may not be sufficiently robust to determine a scale change estimate.
- the system may identify a set of backtracked locations of the set of points in first image data via backtracking the set of points from second image data to the first image data and compare the set of backtracked locations to the original set of locations of the set of points identified from the first image data.
- the system may identify a sub-set of the points from the set of points for which the backtracked locations are not within a threshold distance of the set of first locations (e.g., the locations of the backtracked pixels do not sufficiently match the originally determined locations of the pixels used for forward tracking).
- the system may remove the sub-set of points from the set of points and determine the scale change estimate without the removed sub-set of points. This may improve the accuracy and robustness of the point tracking over a series of image data frames.
- While certain aspects of this disclosure may be performed while the instrument is stationary (e.g., while no robot commands are provided to move the instrument), it may also be desirable to detect physiological noise during dynamic instrument movement (e.g., while driving the instrument within the luminal network).
- dynamic instrument movement e.g., while driving the instrument within the luminal network.
- the change between two image data frames e.g., received at a first time and a second time
- the instrument movement motion should be decoupled from the physiological noise in the motion detected by the image-based algorithm module 970 .
- the system can perform motion decoupling in 3D space by using the 3D movement of the instrument received from positioning sensors (e.g., EM-based state data, robot-based state data, EM and/or optical shape sensing state data, etc.).
- positioning sensors e.g., EM-based state data, robot-based state data, EM and/or optical shape sensing state data, etc.
- the system can employ certain image processing techniques including image-based 3D motion estimation (e.g., structure from motion) to determine the relative 3D motion between the instrument and the luminal network.
- the system may determine a location sensor-based 3D instrument movement between two points in time (e.g., between t 0 and t 1 ) based on data received from locations sensors.
- the 3D instrument movement data may be represented by three spatial degrees-of-freedom (DoF), for example ⁇ x z , y z , z z ⁇ , and three rotational DoF, for example, ⁇ s x , ⁇ s y , ⁇ x z ⁇ .
- DoF spatial degrees-of-freedom
- the system may also determine an image sensor-based 3D instrument movement between the two points in time represented by the same six DoF measurements as in the location sensor-based 3D instrument movement.
- the system may determine a 3D instrument movement estimate representative of physiological movement by determining the difference between the location sensor-based 3D instrument movement and the image sensor-based 3D instrument movement.
- the system may then accumulate the 3D instrument movement estimate representative of physiological movement over a sequence of image data and location sensor measurements over a given time period, from which a frequency and amplitude associated with the physiological noise can be extracted (e.g., using one or more of the above-defined techniques including harmonic analysis).
- the system can detect junction transition by identifying and analyzing the airways from image data received from an image sensor. For example, the system tracks the locations of detected airways between two image data frames (e.g., received at a first time t 0 and a second time t 1 ).
- the system may, in certain embodiments, determine that the instrument has transitioned through a junction in response to at least one of the following conditions being satisfied: 1) all of the estimated airways overlap with the detected airways in the image data at time but there exists one or more detected airways in the image data at time t 0 that do not have an overlap with the estimated airways; 2) all the detected airways in in the image data at time t 1 overlap with the estimated airways, but there exists one or more estimated airways do not have an overlap with the detected airways the image data at time t 0 ; and 3) there exists one or more detected airways that do not overlap with the estimated airways and there exists one or more estimated airways do not overlap with the detected airways.
- embodiments may track the location and sizes of prior airways and compare them to the locations and sizes of airways detected in current image data.
- the presences of one or more of the above listed conditions and the detected of movement of the anatomy relative to the instrument may be used by the system to detect a transition between junctions.
- Implementations disclosed herein provide systems, methods and apparatuses for detecting physiological noise during navigation of a luminal network.
- Couple may indicate either an indirect connection or a direct connection.
- first component may be either indirectly connected to the second component via another component or directly connected to the second component.
- the path-based navigational functions described herein may be stored as one or more instructions on a processor-readable or computer-readable medium.
- the term “computer-readable medium” refers to any available medium that can be accessed by a computer or processor.
- such a medium may comprise random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc read-only memory (CD-ROM) or other optical disk storage may comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer.
- a computer-readable medium may be tangible and non-transitory.
- the term “code” may refer to software, instructions, code or data that is/are executable by a computing device or processor.
- the methods disclosed herein comprise one or more steps or actions for achieving the described method.
- the method steps and/or actions may be interchanged with one another without departing from the scope of the claims.
- the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims.
- the term “plurality” denotes two or more. For example, a plurality of components indicates two or more components.
- the term “determining” encompasses a wide variety of actions and, therefore, “determining” can include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” can include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” can include resolving, selecting, choosing, establishing and the like.
Abstract
Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.
Description
- This application is a continuation of U.S. patent application Ser. No. 16/425,069, filed May 29, 2019, which claims the benefit of U.S. Provisional Application No. 62/678,520, filed May 31, 2018, which are hereby incorporated by reference in their entirety.
- The systems and methods disclosed herein are directed to surgical robotics, and more particularly to endoluminal navigation.
- Bronchoscopy is a medical procedure that allows a physician to examine the inside conditions of a patient's lung airways, such as bronchi and bronchioles. The lung airways carry air from the trachea, or windpipe, to the lungs. During the medical procedure, a thin, flexible tubular tool, known as a bronchoscope, may be inserted into the patient's mouth and passed down the patient's throat into his/her lung airways, and patients are generally anesthetized in order to relax their throats and lung cavities for surgical examinations and operations during the medical procedure.
- In the related art, a bronchoscope can include a light source and a small camera that allows a physician to inspect a patient's windpipe and airways, and a rigid tube may be used in conjunction with the bronchoscope for surgical purposes, e.g., when there is a significant amount of bleeding in the lungs of the patient or when a large object obstructs the throat of the patient. When the rigid tube is used, the patient is often anesthetized. Robotic bronchoscopes provide tremendous advantages in navigation through tubular networks. They can ease use and allow therapies and biopsies to be administered conveniently even during the bronchoscopy stage.
- Apart from mechanical devices or platforms, e.g., robotic bronchoscopes described above, various methods and software models may be used to help with the surgical operations. As an example, a computerized tomography (CT) scan of the patient's lungs is often performed during pre-operation of a surgical examination. Data from the CT scan may be used to generate a three-dimensional (3D) model of airways of the patient's lungs, and the generated 3D model enables a physician to access a visual reference that may be useful during the operative procedure of the surgical examination.
- However, previous techniques for navigation of tubular networks still have challenges, even when employing medical devices (e.g., robotic bronchoscopes) and when using existing methods (e.g., performing CT scans and generating 3D models). As one example, motion estimation of a medical device (e.g., a bronchoscope tool) inside a patient's body may not be accurate based on location and orientation change of the device, and as a result the device's position may not be accurately or correctly localized inside the patient's body in real time. Inaccurate location information for such an instrument may provide misleading information to the physician that uses the 3D model as a visual reference during medical operation procedures.
- Thus, there is a need for improved techniques for navigating through a network of tubular structures.
- In one aspect, there is provided a medical robotic system, comprising a set of one or more processors; and at least one computer-readable memory in communication with the set of processors and having stored thereon computer-executable instructions to cause the set of processors to: receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient, detect a set of one or more points of interest the first image data, identify a set of first locations respectively corresponding to the set of points in the first image data, receive second image data from the image sensor, detect the set of one or more points in the second image data, identify a set of second locations respectively corresponding to the set of points in the second image data, and based on the set of first locations and the set of second locations, detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
- In another aspect, there is provided a non-transitory computer readable storage medium having stored thereon instructions that, when executed, cause at least one computing device to: receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient; detect a set of one or more points of interest the first image data; identify a set of first locations respectively corresponding to the set of points in the first image data; receive second image data from the image sensor; detect the set of one or more points in the second image data; identify a set of second locations respectively corresponding to the set of points in the second image data; and based on the set of first locations and the set of second locations, detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
- In yet another aspect, there is provided a method for detecting a change of location of an instrument, comprising: receiving first image data from an image sensor located on the instrument, the instrument configured to be driven through a luminal network of a patient; detecting a set of one or more points of interest the first image data; identifying a set of first locations respectively corresponding to the set of points in the first image data; receiving second image data from the image sensor; detecting the set of one or more points in the second image data; identifying a set of second locations respectively corresponding to the set of points in the second image data; and based on the set of first locations and the set of second locations, detecting the change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
- The disclosed aspects will hereinafter be described in conjunction with the appended drawings, provided to illustrate and not to limit the disclosed aspects, wherein like designations denote like elements.
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FIG. 1A shows an example surgical robotic system, according to one embodiment. -
FIGS. 1B-1F show various perspective views of a robotic platform coupled to the surgical robotic system shown inFIG. 1A , according to one embodiment. -
FIG. 2 shows an example command console for the example surgical robotic system, according to one embodiment. -
FIG. 3A shows an isometric view of an example independent drive mechanism of the instrument device manipulator (IDM) shown inFIG. 1A , according to one embodiment. -
FIG. 3B shows a conceptual diagram that shows how forces may be measured by a strain gauge of the independent drive mechanism shown inFIG. 3A , according to one embodiment. -
FIG. 4A shows a top view of an example endoscope, according to one embodiment. -
FIG. 4B shows an example endoscope tip of the endoscope shown inFIG. 4A , according to one embodiment. -
FIG. 5 shows an example schematic setup of an EM tracking system included in a surgical robotic system, according to one embodiment. -
FIGS. 6A-6B show an example anatomical lumen and an example 3D model of the anatomical lumen, according to one embodiment. -
FIG. 7 shows a computer-generated 3D model representing an anatomical space, according to one embodiment. -
FIGS. 8A-8D show example graphs illustrating on-the-fly registration of an EM system to a 3D model of a path through a tubular network, according to one embodiment. -
FIGS. 8E-8F show effect of an example registration of the EM system to a 3D model of a branched tubular network, according to one embodiment. -
FIG. 9A shows a high-level overview of an example block diagram of a navigation configuration system, according to one embodiment. -
FIG. 9B shows an example block diagram of the navigation module shown inFIG. 9A , according to one embodiment. -
FIG. 9C shows example block diagram of the estimated state data store included in the state estimator, according to one embodiment. -
FIG. 10A shows an example block diagram of the motion estimation module in accordance with aspects of this disclosure. -
FIG. 10B shows an example block diagram of the object detection module, according to one example. -
FIG. 10C shows an example block diagram of the object mapping module, according to one embodiment. -
FIG. 10D shows an example block diagram of the topological reasoning module, according to one embodiment. -
FIGS. 11A-11B , show an example object-to-lumen mapping performed by the object mapping module, according to one embodiment. -
FIG. 12 is a flowchart illustrating an example method operable by a robotic system, or component(s) thereof, for detecting physiological noise in accordance with aspects of this disclosure. -
FIG. 13A illustrates example image data captured by an image sensor at a first point in time in accordance with aspects of this disclosure. -
FIG. 13B illustrates another example of image data captured by an image sensor at a second point in time, after the first point in time, in accordance with aspects of this disclosure. -
FIG. 13C illustrates an example of the change in location of example pixels between the image data frames illustrated inFIGS. 13A-13B in accordance with aspects of this disclosure. -
FIG. 13D illustrates another example of the change in location of example pixels between the image data frames illustrated inFIGS. 13A-13B in accordance with aspects of this disclosure. -
FIGS. 14A-14B illustrate an example of two image data frames within a sequence of image data frames for which the scale change value may be accumulated in accordance with aspects of this disclosure. -
FIGS. 15A-15B are graphs which illustrate the changes to an accumulated scale change value over a sequence of image data frames in accordance with aspects of this disclosure. - Reference will now be made in detail to several embodiments, examples of which are illustrated in the accompanying figures. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the described system (or method) for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.
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FIG. 1A shows an example surgicalrobotic system 100, according to one embodiment. The surgicalrobotic system 100 includes a base 101 coupled to one or more robotic arms, e.g.,robotic arm 102. Thebase 101 is communicatively coupled to a command console, which is further described with reference toFIG. 2 in Section II. Command Console. The base 101 can be positioned such that therobotic arm 102 has access to perform a surgical procedure on a patient, while a user such as a physician may control the surgicalrobotic system 100 from the comfort of the command console. In some embodiments, thebase 101 may be coupled to a surgical operating table or bed for supporting the patient. Though not shown inFIG. 1 for purposes of clarity, thebase 101 may include subsystems such as control electronics, pneumatics, power sources, optical sources, and the like. Therobotic arm 102 includesmultiple arm segments 110 coupled atjoints 111, which provides therobotic arm 102 multiple degrees of freedom, e.g., seven degrees of freedom corresponding to seven arm segments. The base 101 may contain a source ofpower 112,pneumatic pressure 113, and control andsensor electronics 114—including components such as a central processing unit, data bus, control circuitry, and memory—and related actuators such as motors to move therobotic arm 102. Theelectronics 114 in thebase 101 may also process and transmit control signals communicated from the command console. - In some embodiments, the
base 101 includeswheels 115 to transport the surgicalrobotic system 100. Mobility of the surgicalrobotic system 100 helps accommodate space constraints in a surgical operating room as well as facilitate appropriate positioning and movement of surgical equipment. Further, the mobility allows therobotic arms 102 to be configured such that therobotic arms 102 do not interfere with the patient, physician, anesthesiologist, or any other equipment. During procedures, a user may control therobotic arms 102 using control devices such as the command console. - In some embodiments, the
robotic arm 102 includes set up joints that use a combination of brakes and counter-balances to maintain a position of therobotic arm 102. The counter-balances may include gas springs or coil springs. The brakes, e.g., fail safe brakes, may be include mechanical and/or electrical components. Further, therobotic arms 102 may be gravity-assisted passive support type robotic arms. - Each
robotic arm 102 may be coupled to an instrument device manipulator (IDM) 117 using a mechanism changer interface (MCI) 116. TheIDM 117 can be removed and replaced with a different type of IDM, for example, a first type of IDM manipulates an endoscope, while a second type of IDM manipulates a laparoscope. TheMCI 116 includes connectors to transfer pneumatic pressure, electrical power, electrical signals, and optical signals from therobotic arm 102 to theIDM 117. TheMCI 116 can be a set screw or base plate connector. TheIDM 117 manipulates surgical instruments such as theendoscope 118 using techniques including direct drive, harmonic drive, geared drives, belts and pulleys, magnetic drives, and the like. TheMCI 116 is interchangeable based on the type ofIDM 117 and can be customized for a certain type of surgical procedure. The robotic 102 arm can include a joint level torque sensing and a wrist at a distal end, such as the KUKA AG® LBR5 robotic arm. - The
endoscope 118 is a tubular and flexible surgical instrument that is inserted into the anatomy of a patient to capture images of the anatomy (e.g., body tissue). In particular, theendoscope 118 includes one or more imaging devices (e.g., cameras or other types of optical sensors) that capture the images. The imaging devices may include one or more optical components such as an optical fiber, fiber array, or lens. The optical components move along with the tip of theendoscope 118 such that movement of the tip of theendoscope 118 results in changes to the images captured by the imaging devices. Theendoscope 118 is further described with reference toFIGS. 3A-4B in Section IV. Endoscope. -
Robotic arms 102 of the surgicalrobotic system 100 manipulate theendoscope 118 using elongate movement members. The elongate movement members may include pull wires, also referred to as pull or push wires, cables, fibers, or flexible shafts. For example, therobotic arms 102 actuate multiple pull wires coupled to theendoscope 118 to deflect the tip of theendoscope 118. The pull wires may include both metallic and non-metallic materials such as stainless steel, Kevlar, tungsten, carbon fiber, and the like. Theendoscope 118 may exhibit nonlinear behavior in response to forces applied by the elongate movement members. The nonlinear behavior may be based on stiffness and compressibility of theendoscope 118, as well as variability in slack or stiffness between different elongate movement members. -
FIGS. 1B-1F show various perspective views of the surgicalrobotic system 100 coupled to a robotic platform 150 (or surgical bed), according to various embodiments. Specifically,FIG. 1B shows a side view of the surgicalrobotic system 100 with therobotic arms 102 manipulating the endoscopic 118 to insert the endoscopic inside a patient's body, and the patient is lying on therobotic platform 150.FIG. 1C shows a top view of the surgicalrobotic system 100 and therobotic platform 150, and the endoscopic 118 manipulated by the robotic arms is inserted inside the patient's body.FIG. 1D shows a perspective view of the surgicalrobotic system 100 and therobotic platform 150, and the endoscopic 118 is controlled to be positioned horizontally parallel with the robotic platform.FIG. 1E shows another perspective view of the surgicalrobotic system 100 and therobotic platform 150, and the endoscopic 118 is controlled to be positioned relatively perpendicular to the robotic platform. In more detail, inFIG. 1E , the angle between the horizontal surface of therobotic platform 150 and the endoscopic 118 is 75 degree.FIG. 1F shows the perspective view of the surgicalrobotic system 100 and therobotic platform 150 shown inFIG. 1E , and in more detail, the angle between the endoscopic 118 and thevirtual line 160 connecting oneend 180 of the endoscopic and therobotic arm 102 that is positioned relatively farther away from the robotic platform is 90 degree. -
FIG. 2 shows anexample command console 200 for the example surgicalrobotic system 100, according to one embodiment. Thecommand console 200 includes aconsole base 201,display modules 202, e.g., monitors, and control modules, e.g., akeyboard 203 andjoystick 204. In some embodiments, one or more of thecommand console 200 functionality may be integrated into abase 101 of the surgicalrobotic system 100 or another system communicatively coupled to the surgicalrobotic system 100. A user 205, e.g., a physician, remotely controls the surgicalrobotic system 100 from an ergonomic position using thecommand console 200. - The
console base 201 may include a central processing unit, a memory unit, a data bus, and associated data communication ports that are responsible for interpreting and processing signals such as camera imagery and tracking sensor data, e.g., from theendoscope 118 shown inFIG. 1 . In some embodiments, both theconsole base 201 and the base 101 perform signal processing for load-balancing. Theconsole base 201 may also process commands and instructions provided by the user 205 through thecontrol modules keyboard 203 andjoystick 204 shown inFIG. 2 , the control modules may include other devices, for example, computer mice, trackpads, trackballs, control pads, video game controllers, and sensors (e.g., motion sensors or cameras) that capture hand gestures and finger gestures. - The user 205 can control a surgical instrument such as the
endoscope 118 using thecommand console 200 in a velocity mode or position control mode. In velocity mode, the user 205 directly controls pitch and yaw motion of a distal end of theendoscope 118 based on direct manual control using the control modules. For example, movement on thejoystick 204 may be mapped to yaw and pitch movement in the distal end of theendoscope 118. Thejoystick 204 can provide haptic feedback to the user 205. For example, thejoystick 204 vibrates to indicate that theendoscope 118 cannot further translate or rotate in a certain direction. Thecommand console 200 can also provide visual feedback (e.g., pop-up messages) and/or audio feedback (e.g., beeping) to indicate that theendoscope 118 has reached maximum translation or rotation. - In position control mode, the
command console 200 uses a three-dimensional (3D) map of a patient and pre-determined computer models of the patient to control a surgical instrument, e.g., theendoscope 118. Thecommand console 200 provides control signals torobotic arms 102 of the surgicalrobotic system 100 to manipulate theendoscope 118 to a target location. Due to the reliance on the 3D map, position control mode requires accurate mapping of the anatomy of the patient. - In some embodiments, users 205 can manually manipulate
robotic arms 102 of the surgicalrobotic system 100 without using thecommand console 200. During setup in a surgical operating room, the users 205 may move therobotic arms 102,endoscopes 118, and other surgical equipment to access a patient. The surgicalrobotic system 100 may rely on force feedback and inertia control from the users 205 to determine appropriate configuration of therobotic arms 102 and equipment. - The
display modules 202 may include electronic monitors, virtual reality viewing devices, e.g., goggles or glasses, and/or other means of display devices. In some embodiments, thedisplay modules 202 are integrated with the control modules, for example, as a tablet device with a touchscreen. Further, the user 205 can both view data and input commands to the surgicalrobotic system 100 using the integrateddisplay modules 202 and control modules. - The
display modules 202 can display 3D images using a stereoscopic device, e.g., a visor or goggle. The 3D images provide an “endo view” (i.e., endoscopic view), which is acomputer 3D model illustrating the anatomy of a patient. The “endo view” provides a virtual environment of the patient's interior and an expected location of anendoscope 118 inside the patient. A user 205 compares the “endo view” model to actual images captured by a camera to help mentally orient and confirm that theendoscope 118 is in the correct—or approximately correct—location within the patient. The “endo view” provides information about anatomical structures, e.g., the shape of an intestine or colon of the patient, around the distal end of theendoscope 118. Thedisplay modules 202 can simultaneously display the 3D model and computerized tomography (CT) scans of the anatomy the around distal end of theendoscope 118. Further, thedisplay modules 202 may overlay the already determined navigation paths of theendoscope 118 on the 3D model and scans/images generated based on preoperative model data (e.g., CT scans). - In some embodiments, a model of the
endoscope 118 is displayed with the 3D models to help indicate a status of a surgical procedure. For example, the CT scans identify a lesion in the anatomy where a biopsy may be necessary. During operation, thedisplay modules 202 may show a reference image captured by theendoscope 118 corresponding to the current location of theendoscope 118. Thedisplay modules 202 may automatically display different views of the model of theendoscope 118 depending on user settings and a particular surgical procedure. For example, thedisplay modules 202 show an overhead fluoroscopic view of theendoscope 118 during a navigation step as theendoscope 118 approaches an operative region of a patient. -
FIG. 3A shows an isometric view of an example independent drive mechanism of theIDM 117 shown inFIG. 1 , according to one embodiment. The independent drive mechanism can tighten or loosen thepull wires output shafts IDM 117, respectively. Just as theoutput shafts wires pull wires IDM 117 and/or the surgicalrobotic system 100 can measure the transferred force using a sensor, e.g., a strain gauge further described below. -
FIG. 3B shows a conceptual diagram that shows how forces may be measured by astrain gauge 334 of the independent drive mechanism shown inFIG. 3A , according to one embodiment. Aforce 331 may direct away from theoutput shaft 305 coupled to themotor mount 333 of themotor 337. Accordingly, theforce 331 results in horizontal displacement of themotor mount 333. Further, thestrain gauge 334 horizontally coupled to themotor mount 333 experiences strain in the direction of theforce 331. The strain may be measured as a ratio of the horizontal displacement of the tip 335 ofstrain gauge 334 to the overallhorizontal width 336 of thestrain gauge 334. - In some embodiments, the
IDM 117 includes additional sensors, e.g., inclinometers or accelerometers, to determine an orientation of theIDM 117. Based on measurements from the additional sensors and/or thestrain gauge 334, the surgicalrobotic system 100 can calibrate readings from thestrain gauge 334 to account for gravitational load effects. For example, if theIDM 117 is oriented on a horizontal side of theIDM 117, the weight of certain components of theIDM 117 may cause a strain on themotor mount 333. Accordingly, without accounting for gravitational load effects, thestrain gauge 334 may measure strain that did not result from strain on the output shafts. -
FIG. 4A shows a top view of anexample endoscope 118, according to one embodiment. Theendoscope 118 includes aleader 415 tubular component nested or partially nested inside and longitudinally-aligned with asheath 411 tubular component. Thesheath 411 includes aproximal sheath section 412 anddistal sheath section 413. Theleader 415 has a smaller outer diameter than thesheath 411 and includes aproximal leader section 416 anddistal leader section 417. Thesheath base 414 and theleader base 418 actuate thedistal sheath section 413 and thedistal leader section 417, respectively, for example, based on control signals from a user of a surgicalrobotic system 100. Thesheath base 414 and theleader base 418 are, e.g., part of theIDM 117 shown inFIG. 1 . - Both the
sheath base 414 and theleader base 418 include drive mechanisms (e.g., the independent drive mechanism further described with reference toFIG. 3A-B in Section III. Instrument Device Manipulator) to control pull wires coupled to thesheath 411 andleader 415. For example, thesheath base 414 generates tensile loads on pull wires coupled to thesheath 411 to deflect thedistal sheath section 413. Similarly, theleader base 418 generates tensile loads on pull wires coupled to theleader 415 to deflect thedistal leader section 417. Both thesheath base 414 andleader base 418 may also include couplings for the routing of pneumatic pressure, electrical power, electrical signals, or optical signals from IDMs to thesheath 411 andleader 414, respectively. A pull wire may include a steel coil pipe along the length of the pull wire within thesheath 411 or theleader 415, which transfers axial compression back to the origin of the load, e.g., thesheath base 414 or theleader base 418, respectively. - The
endoscope 118 can navigate the anatomy of a patient with ease due to the multiple degrees of freedom provided by pull wires coupled to thesheath 411 and theleader 415. For example, four or more pull wires may be used in either thesheath 411 and/or theleader 415, providing eight or more degrees of freedom. In other embodiments, up to three pull wires may be used, providing up to six degrees of freedom. Thesheath 411 andleader 415 may be rotated up to 360 degrees along alongitudinal axis 406, providing more degrees of motion. The combination of rotational angles and multiple degrees of freedom provides a user of the surgicalrobotic system 100 with a user friendly and instinctive control of theendoscope 118. -
FIG. 4B illustrates anexample endoscope tip 430 of theendoscope 118 shown inFIG. 4A , according to one embodiment. InFIG. 4B , theendoscope tip 430 includes an imaging device 431 (e.g., a camera),illumination sources 432, and ends of EM coils 434. Theillumination sources 432 provide light to illuminate an interior portion of an anatomical space. The provided light allows theimaging device 431 to record images of that space, which can then be transmitted to a computer system such ascommand console 200 for processing as described herein. Electromagnetic (EM) coils 434 located on thetip 430 may be used with an EM tracking system to detect the position and orientation of theendoscope tip 430 while it is disposed within an anatomical system. In some embodiments, the coils may be angled to provide sensitivity to EM fields along different axes, giving the ability to measure a full 6 degrees of freedom: three positional and three angular. In other embodiments, only a single coil may be disposed within theendoscope tip 430, with its axis oriented along the endoscope shaft of theendoscope 118; due to the rotational symmetry of such a system, it is insensitive to roll about its axis, so only 5 degrees of freedom may be detected in such a case. Theendoscope tip 430 further comprises a workingchannel 436 through which surgical instruments, such as biopsy needles, may be inserted along the endoscope shaft, allowing access to the area near the endoscope tip. -
FIG. 5 shows an example schematic setup of anEM tracking system 505 included in a surgicalrobotic system 500, according to one embodiment. InFIG. 5 , multiple robot components (e.g., window field generator, reference sensors as described below) are included in theEM tracking system 505. The roboticsurgical system 500 includes asurgical bed 511 to hold a patient's body. Beneath thebed 511 is the window field generator (WFG) 512 configured to sequentially activate a set of EM coils (e.g., the EM coils 434 shown inFIG. 4B ). TheWFG 512 generates an alternating current (AC) magnetic field over a wide volume; for example, in some cases it may create an AC field in a volume of about 0.5×0.5×0.5 m. - Additional fields may be applied by further field generators to aid in tracking instruments within the body. For example, a planar field generator (PFG) may be attached to a system arm adjacent to the patient and oriented to provide an EM field at an angle.
Reference sensors 513 may be placed on the patient's body to provide local EM fields to further increase tracking accuracy. Each of thereference sensors 513 may be attached bycables 514 to acommand module 515. Thecables 514 are connected to thecommand module 515 throughinterface units 516 which handle communications with their respective devices as well as providing power. Theinterface unit 516 is coupled to a system control unit (SCU) 517 which acts as an overall interface controller for the various entities mentioned above. TheSCU 517 also drives the field generators (e.g., WFG 512), as well as collecting sensor data from theinterface units 516, from which it calculates the position and orientation of sensors within the body. TheSCU 517 may be coupled to a personal computer (PC) 518 to allow user access and control. - The
command module 515 is also connected to thevarious IDMs 519 coupled to the surgicalrobotic system 500 as described herein. TheIDMs 519 are typically coupled to a single surgical robotic system (e.g., the surgical robotic system 500) and are used to control and receive data from their respective connected robotic components; for example, robotic endoscope tools or robotic arms. As described above, as an example, theIDMs 519 are coupled to an endoscopic tool (not shown here) of the surgicalrobotic system 500 - The
command module 515 receives data passed from the endoscopic tool. The type of received data depends on the corresponding type of instrument attached. For example, example received data includes sensor data (e.g., image data, EM data), robot data (e.g., endoscopic and IDM physical motion data), control data, and/or video data. To better handle video data, a field-programmable gate array (FPGA) 520 may be configured to handle image processing. Comparing data obtained from the various sensors, devices, and field generators allows theSCU 517 to precisely track the movements of different components of the surgicalrobotic system 500, and for example, positions and orientations of these components. - In order to track a sensor through the patient's anatomy, the
EM tracking system 505 may require a process known as “registration,” where the system finds the geometric transformation that aligns a single object between different coordinate systems. For instance, a specific anatomical site on a patient has two different representations in the 3D model coordinates and in the EM sensor coordinates. To be able to establish consistency and common language between these two different coordinate systems, theEM tracking system 505 needs to find the transformation that links these two representations, i.e., registration. For example, the position of the EM tracker relative to the position of the EM field generator may be mapped to a 3D coordinate system to isolate a location in a corresponding 3D model. -
FIGS. 6A-6B show an exampleanatomical lumen 600 and anexample 3D model 620 of the anatomical lumen, according to one embodiment. More specifically,FIGS. 6A-6B illustrate the relationships of centerline coordinates, diameter measurements and anatomical spaces between the actualanatomical lumen 600 and its3D model 620. InFIG. 6A , theanatomical lumen 600 is roughly tracked longitudinally by centerline coordinates 601, 602, 603, 604, 605, and 606 where each centerline coordinate roughly approximates the center of the tomographic slice of the lumen. The centerline coordinates are connected and visualized by acenterline 607. The volume of the lumen can be further visualized by measuring the diameter of the lumen at each centerline coordinate, e.g., coordinates 608, 609, 610, 611, 612, and 613 represent the measurements of thelumen 600 corresponding tocoordinates -
FIG. 6B shows theexample 3D model 620 of theanatomical lumen 600 shown inFIG. 6A , according to one embodiment. InFIG. 6B , theanatomical lumen 600 is visualized in 3D space by first locating the centerline coordinates 601, 602, 603, 604, 605, and 606 in 3D space based on thecenterline 607. As one example, at each centerline coordinate, the lumen diameter is visualized as a 2D circular space (e.g., the 2D circular space 630) withdiameters anatomical lumen 600 is approximated and visualized as the3D model 620. More accurate approximations may be determined by increasing the resolution of the centerline coordinates and measurements, i.e., increasing the density of centerline coordinates and measurements for a given lumen or subsection. Centerline coordinates may also include markers to indicate point of interest for the physician, including lesions. - In some embodiments, a pre-operative software package is also used to analyze and derive a navigation path based on the generated 3D model of the anatomical space. For example, the software package may derive a shortest navigation path to a single lesion (marked by a centerline coordinate) or to several lesions. This navigation path may be presented to the operator intra-operatively either in two-dimensions or three-dimensions depending on the operator's preference. In certain implementations, the navigation path (or at a portion thereof) may be pre-operatively selected by the operator. The path selection may include identification of one or more target locations (also simply referred to as a “target”) within the patient's anatomy.
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FIG. 7 shows a computer-generated3D model 700 representing an anatomical space, according to one embodiment. As discussed above inFIGS. 6A-6B , the3D model 700 may be generated using acenterline 701 that was obtained by reviewing CT scans that were generated preoperatively. In some embodiments, computer software may be able to map anavigation path 702 within the tubular network to access an operative site 703 (or other target) within the3D model 700. In some embodiments, theoperative site 703 may be linked to an individual centerline coordinate 704, which allows a computer algorithm to topologically search the centerline coordinates of the3D model 700 for theoptimum path 702 within the tubular network. In certain embodiments, the topological search for thepath 702 may be constrained by certain operator selected parameters, such as the location of one or more targets, one or more waypoints, etc. - In some embodiments, the distal end of the endoscopic tool within the patient's anatomy is tracked, and the tracked location of the endoscopic tool within the patient's anatomy is mapped and placed within a computer model, which enhances the navigational capabilities of the tubular network. In order to track the distal working end of the endoscopic tool, i.e., location and orientation of the working end, a number of approaches may be employed, either individually or in combination.
- In a sensor-based approach to localization, a sensor, such as an EM tracker, may be coupled to the distal working end of the endoscopic tool to provide a real-time indication of the progression of the endoscopic tool. In EM-based tracking, an EM tracker, embedded in the endoscopic tool, measures the variation in the electromagnetic field created by one or more EM transmitters. The transmitters (or field generators), may be placed close to the patient (e.g., as part of the surgical bed) to create a low intensity magnetic field. This induces small-currents in sensor coils in the EM tracker, which are correlated to the distance and angle between the sensor and the generator. The electrical signal may then be digitized by an interface unit (on-chip or PCB) and sent via cables/wiring back to the system cart and then to the command module. The data may then be processed to interpret the current data and calculate the precise location and orientation of the sensor relative to the transmitters. Multiple sensors may be used at different locations in the endoscopic tool, for instance in leader and sheath in order to calculate the individual positions of those components. Accordingly, based on readings from an artificially-generated EM field, the EM tracker may detect changes in field strength as it moves through the patient's anatomy.
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FIGS. 8A-8D show example graphs 810-840 illustrating on-the-fly registration of an EM system to a 3D model of a path through a tubular network, according to one embodiment. The navigation configuration system described herein allows for on-the-fly registration of the EM coordinates to the 3D model coordinates without the need for independent registration prior to an endoscopic procedure. In more detail,FIG. 8A shows that the coordinate systems of the EM tracking system and the 3D model are initially not registered to each other, and the graph 810 inFIG. 8A shows the registered (or expected) location of anendoscope tip 801 moving along a plannednavigation path 802 through a branched tubular network (not shown here), and the registered location of theinstrument tip 801 as well as theplanned path 802 are derived from the 3D model. The actual position of the tip is repeatedly measured by theEM tracking system 505, resulting in multiple measuredlocation data points 803 based on EM data. As shown inFIG. 8A , the data points 803 derived from EM tracking are initially located far from the registered location of theendoscope tip 801 expected from the 3D model, reflecting the lack of registration between the EM coordinates and the 3D model coordinates. There may be several reasons for this, for example, even if the endoscope tip is being moved relatively smoothly through the tubular network, there may still be some visible scatter in the EM measurement, due to breathing movement of the lungs of the patient. - The points on the 3D model may also be determined and adjusted based on correlation between the 3D model itself, image data received from optical sensors (e.g., cameras) and robot data from robot commands. The 3D transformation between these points and collected EM data points will determine the initial registration of the EM coordinate system to the 3D model coordinate system.
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FIG. 8B shows agraph 820 at a later temporal stage compared with the graph 810, according to one embodiment. More specifically, thegraph 820 shows the expected location of theendoscope tip 801 expected from the 3D model has been moved farther along thepreplanned navigation path 802, as illustrated by the shift from the original expected position of theinstrument tip 801 shown inFIG. 8A along the path to the position shown inFIG. 8B . During the EM tracking between generation of the graph 810 and generation ofgraph 820,additional data points 803 have been recorded by the EM tracking system but the registration has not yet been updated based on the newly collected EM data. As a result, the data points 803 inFIG. 8B are clustered along avisible path 814, but that path differs in location and orientation from the plannednavigation path 802 the endoscope tip is being directed by the operator to travel along. Eventually, once sufficient data (e.g., EM data) is accumulated, compared with using only the 3D model or only the EM data, a relatively more accurate estimate can be derived from the transform needed to register the EM coordinates to those of the 3D model. The determination of sufficient data may be made by threshold criteria such as total data accumulated or number of changes of direction. For example, in a branched tubular network such as a bronchial tube network, it may be judged that sufficient data have been accumulated after arriving at two branch points. -
FIG. 8C shows agraph 830 shortly after the navigation configuration system has accumulated a sufficient amount of data to estimate the registration transform from EM to 3D model coordinates, according to one embodiment. The data points 803 inFIG. 8C have now shifted from their previous position as shown inFIG. 8B as a result of the registration transform. As shown inFIG. 8C , the data points 803 derived from EM data is now falling along the plannednavigation path 802 derived from the 3D model, and each data point among the data points 803 is now reflecting a measurement of the expected position ofendoscope tip 801 in the coordinate system of the 3D model. In some embodiments, as further data are collected, the registration transform may be updated to increase accuracy. In some cases, the data used to determine the registration transformation may be a subset of data chosen by a moving window, so that the registration may change over time, which gives the ability to account for changes in the relative coordinates of the EM and 3D models—for example, due to movement of the patient. -
FIG. 8D shows anexample graph 840 in which the expected location of theendoscope tip 801 has reached the end of the plannednavigation path 802, arriving at the target location in the tubular network, according to one embodiment. As shown inFIG. 8D , the recorded EM data points 803 is now generally tracks along the plannednavigation path 802, which represents the tracking of the endoscope tip throughout the procedure. Each data point reflects a transformed location due to the updated registration of the EM tracking system to the 3D model. - In some embodiments, each of the graphs shown in
FIGS. 8A-8D can be shown sequentially on a display visible to a user as the endoscope tip is advanced in the tubular network. In some embodiments, the processor can be configured with instructions from the navigation configuration system such that the model shown on the display remains substantially fixed when the measured data points are registered to the display by shifting of the measured path shown on the display in order to allow the user to maintain a fixed frame of reference and to remain visually oriented on the model and on the planned path shown on the display. -
FIGS. 8E-8F show the effect of an example registration of the EM system to a 3D model of a branched tubular network, according to one embodiment. InFIGS. 8E-8F , 3D graphs showingelectromagnetic tracking data 852 and a model of a patient'sbronchial system 854 are illustrated without (shown inFIG. 8E ) and with (shown inFIG. 8F ) a registration transform. InFIG. 8E , without registration, tracking data 860 have a shape that corresponds to a path through thebronchial system 854, but that shape is subjected to an arbitrary offset and rotation. InFIG. 8F , by applying the registration, the trackingdata 852 are shifted and rotated, so that they correspond to a path through thebronchial system 854. -
FIGS. 9A-9C show example block diagrams of anavigation configuration system 900, according to one embodiment. More specifically,FIG. 9A shows a high-level overview of an example block diagram of thenavigation configuration system 900, according to one embodiment. InFIG. 9A , thenavigation configuration system 900 includes multiple input data stores, anavigation module 905 that receives various types of input data from the multiple input data stores, and an outputnavigation data store 990 that receives output navigation data from the navigation module. The block diagram of thenavigation configuration system 900 shown inFIG. 9A is merely one example, and in alternative embodiments not shown, thenavigation configuration system 900 can include different and/or addition entities. Likewise, functions performed by various entities of thesystem 900 may differ according to different embodiments. Thenavigation configuration system 900 may be similar to the navigational system described in U.S. Patent Publication No. 2017/0084027, published on Mar. 23, 2017, the entirety of which is incorporated herein by reference. - The input data, as used herein, refers to raw data gathered from and/or processed by input devices (e.g., command module, optical sensor, EM sensor, IDM) for generating estimated state information for the endoscope as well as output navigation data. The multiple input data stores 910-940 include an
image data store 910, anEM data store 920, arobot data store 930, and a 3Dmodel data store 940. Each type of the input data stores 910-940 stores the name-indicated type of data for access and use by anavigation module 905. Image data may include one or more image frames captured by the imaging device at the instrument tip, as well as information 911 such as frame rates or timestamps that allow a determination of the time elapsed between pairs of frames. Robot data may include data related to physical movement of the medical instrument or part of the medical instrument (e.g., the instrument tip or sheath) within the tubular network. Example robot data includes command data instructing the instrument tip to reach a specific anatomical site and/or change its orientation (e.g., with a specific pitch, roll, yaw, insertion, and retraction for one or both of a leader and a sheath) within the tubular network, insertion data representing insertion movement of the part of the medical instrument (e.g., the instrument tip or sheath), IDM data, and mechanical data representing mechanical movement of an elongate member of the medical instrument, for example motion of one or more pull wires, tendons or shafts of the endoscope that drive the actual movement of the medial instrument within the tubular network. EM data may be collected by EM sensors and/or the EM tracking system as described above. 3D model data may be derived from 2D CT scans as described above. - The output
navigation data store 990 receives and stores output navigation data provided by thenavigation module 905. Output navigation data indicates information to assist in directing the medical instrument through the tubular network to arrive at a particular destination within the tubular network, and is based on estimated state information for the medical instrument at each instant time, the estimated state information including the location and orientation of the medical instrument within the tubular network. In one embodiment, as the medical instrument moves inside the tubular network, the output navigation data indicating updates of movement and location/orientation information of the medical instrument is provided in real time, which better assists its navigation through the tubular network. - To determine the output navigation data, the
navigation module 905 locates (or determines) the estimated state of the medical instrument within a tubular network. As shown inFIG. 9A , thenavigation module 905 further includes various algorithm modules, such as an EM-basedalgorithm module 950, an image-basedalgorithm module 960, and a robot-basedalgorithm module 970, that each may consume mainly certain types of input data and contribute a different type of data to astate estimator 980. As illustrated inFIG. 9A , the different kinds of data output by these modules, labeled EM-based data, the image-based data, and the robot-based data, may be generally referred to as “intermediate data” for sake of explanation. The detailed composition of each algorithm module and of thestate estimator 980 is more fully described below. -
FIG. 9B shows an example block diagram of thenavigation module 905 shown inFIG. 9A , according to one embodiment. As introduced above, thenavigation module 905 further includes astate estimator 980 as well as multiple algorithm modules that employ different algorithms for navigating through a tubular network. For clarity of description, thestate estimator 980 is described first, followed by the description of the various modules that exchange data with thestate estimator 980. - The
state estimator 980 included in thenavigation module 905 receives various intermediate data and provides the estimated state of the instrument tip as a function of time, where the estimated state indicates the estimated location and orientation information of the instrument tip within the tubular network. The estimated state data are stored in the estimateddata store 985 that is included in thestate estimator 980. -
FIG. 9C shows an example block diagram of the estimatedstate data store 985 included in thestate estimator 980, according to one embodiment. The estimatedstate data store 985 may include abifurcation data store 1086, aposition data store 1087, adepth data store 1088, and anorientation data store 1089, however this particular breakdown of data storage is merely one example, and in alternative embodiments not shown, different and/or additional data stores can be included in the estimatedstate data store 985. - The various stores introduced above represent estimated state data in a variety of ways. Specifically, bifurcation data refers to the location of the medical instrument with respect to the set of branches (e.g., bifurcation, trifurcation or a division into more than three branches) within the tubular network. For example, the bifurcation data can be set of branch choices elected by the instrument as it traverses through the tubular network, based on a larger set of available branches as provided, for example, by the 3D model which maps the entirety of the tubular network. The bifurcation data can further include information in front of the location of the instrument tip, such as branches (bifurcations) that the instrument tip is near but has not yet traversed through, but which may have been detected, for example, based on the tip's current position information relative to the 3D model, or based on images captured of the upcoming bifurcations.
- Position data indicates three-dimensional position of some part of the medical instrument within the tubular network or some part of the tubular network itself. Position data can be in the form of absolute locations or relative locations relative to, for example, the 3D model of the tubular network. As one example, position data can include an indication of the position of the location of the instrument being within a specific branch. The identification of the specific branch may also be stored as a segment identification (ID) which uniquely identifies the specific segment of the model in which the instrument tip is located.
- Depth data indicates depth information of the instrument tip within the tubular network. Example depth data includes the total insertion (absolute) depth of the medical instrument into the patient as well as the (relative) depth within an identified branch (e.g., the segment identified by the position data store 1087). Depth data may be determined based on position data regarding both the tubular network and medical instrument.
- Orientation data indicates orientation information of the instrument tip, and may include overall roll, pitch, and yaw in relation to the 3D model as well as pitch, roll, raw within an identified branch.
- Turning back to
FIG. 9B , thestate estimator 980 provides the estimated state data back to the algorithm modules for generating more accurate intermediate data, which the state estimator uses to generate improved and/or updated estimated states, and so on forming a feedback loop. For example, as shown inFIG. 9B , the EM-basedalgorithm module 950 receives prior EM-based estimated state data, also referred to as data associated with timestamp “t-1.” Thestate estimator 980 uses this data to generate “estimated state data (prior)” that is associated with timestamp “t-1.” Thestate estimator 980 then provides the data back to the EM-based algorithm module. The “estimated state data (prior)” may be based on a combination of different types of intermediate data (e.g., robotic data, image data) that is associated with timestamp “t-1” as generated and received from different algorithm modules. Next, the EM-basedalgorithm module 950 runs its algorithms using the estimated state data (prior) to output to thestate estimator 980 improved and updated EM-based estimated state data, which is represented by “EM-based estimated state data (current)” here and associated with timestamp t. This process continues to repeat for future timestamps as well. - As the
state estimator 980 may use several different kinds of intermediate data to arrive at its estimates of the state of the medical instrument within the tubular network, thestate estimator 980 is configured to account for the various different kinds of errors and uncertainty in both measurement and analysis that each type of underlying data (robotic, EM, image) and each type of algorithm module might create or carry through into the intermediate data used for consideration in determining the estimated state. To address these, two concepts are discussed, that of a probability distribution and that of confidence value. - The “probability” of the “probability distribution”, as used herein, refers to a likelihood of an estimation of a possible location and/or orientation of the medical instrument being correct. For example, different probabilities may be calculated by one of the algorithm modules indicating the relative likelihood that the medical instrument is in one of several different possible branches within the tubular network. In one embodiment, the type of probability distribution (e.g., discrete distribution or continuous distribution) is chosen to match features of an estimated state (e.g., type of the estimated state, for example continuous position information vs. discrete branch choice). As one example, estimated states for identifying which segment the medical instrument is in for a trifurcation may be represented by a discrete probability distribution, and may include three discrete values of 20%, 30% and 50% representing chance as being in the location inside each of the three branches as determined by one of the algorithm modules. As another example, the estimated state may include a roll angle of the medical instrument of 40±5 degrees and a segment depth of the instrument tip within a branch may be is 4±1 mm, each represented by a Gaussian distribution which is a type of continuous probability distribution. Different methods or modalities can be used to generate the probabilities, which will vary by algorithm module as more fully described below with reference to later figures.
- In contrast, the “confidence value,” as used herein, reflects a measure of confidence in the estimation of the state provided by one of the algorithms based one or more factors. For the EM-based algorithms, factors such as distortion to EM Field, inaccuracy in EM registration, shift or movement of the patient, and respiration of the patient may affect the confidence in estimation of the state. Particularly, the confidence value in estimation of the state provided by the EM-based algorithms may depend on the particular respiration cycle of the patient, movement of the patient or the EM field generators, and the location within the anatomy where the instrument tip locates. For the image-based algorithms, examples factors that may affect the confidence value in estimation of the state include illumination condition for the location within the anatomy where the images are captured, presence of fluid, tissue, or other obstructions against or in front of the optical sensor capturing the images, respiration of the patient, condition of the tubular network of the patient itself (e.g., lung) such as the general fluid inside the tubular network and occlusion of the tubular network, and specific operating techniques used in, e.g., navigating or image capturing.
- For example one factor may be that a particular algorithm has differing levels of accuracy at different depths in a patient's lungs, such that relatively close to the airway opening, a particular algorithm may have a high confidence in its estimations of medical instrument location and orientation, but the further into the bottom of the lung the medical instrument travels that confidence value may drop. Generally, the confidence value is based on one or more systemic factors relating to the process by which a result is determined, whereas probability is a relative measure that arises when trying to determine the correct result from multiple possibilities with a single algorithm based on underlying data.
- As one example, a mathematical equation for calculating results of an estimated state represented by a discrete probability distribution (e.g., branch/segment identification for a trifurcation with three values of an estimated state involved) can be as follows:
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S 1 =C EM *P 1,EM +C Image *P 1,Image +C Robot *P 1,Robot; -
S E =C EM *P 0,EM +C Image *P 0,Image +C Robot *P 0,Robot; -
S 3 =C EM *P 3,EM +C Image *P 3,Image +C Robot *P 3,Robot. - In the example mathematical equation above, Si(i=1, 2, 3) represents possible example values of an estimated state in a case where 3 possible segments are identified or present in the 3D model, CEM CImage, and CRobot and represents confidence value corresponding to EM-based algorithm, image-based algorithm, and robot-based algorithm and Pi, EM Pi,Image, andn Pi,Robot represent the probabilities for segment i.
- To better illustrate the concepts of probability distributions and confidence value associated with estimate states, a detailed example is provided here. In this example, a user is trying to identify segment where an instrument tip is located in a certain trifurcation within a central airway (the predicted region) of the tubular network, and three algorithms modules are used including EM-based algorithm, image-based algorithm, and robot-based algorithm. In this example, a probability distribution corresponding to the EM-based algorithm may be 20% in the first branch, 30% in the second branch, and 50% in the third (last) branch, and the confidence value applied to this EM-based algorithm and the central airway is 80%. For the same example, a probability distribution corresponding to the image-based algorithm may be 40%, 20%, 40% for the first, second, and third branch, and the confidence value applied to this image-based algorithm is 30%; while a probability distribution corresponding to the robot-based algorithm may be 10%, 60%, 30% for the first, second, and third branch, and the confidence value applied to this image-based algorithm is 20%. The difference of confidence values applied to the EM-based algorithm and the image-based algorithm indicates that the EM-based algorithm may be a better choice for segment identification in the central airway, compared with the image-based algorithm. An example mathematical calculation of a final estimated state can be:
- for the first branch: 20%*80%+40%*30%+10%*20%=30%; for the second branch: 30%*80%+20%*30%+60%*20%=42%; and for the third branch: 50%*80%+40%*30%+30%*20%=58%.
- In this example, the output estimated state for the instrument tip can be the result values (e.g., the resulting 30%, 42% and 58%), or derivative value from these result values such as the determination that the instrument tip is in the third branch.
- As above the estimated state may be represented in a number of different ways. For example, the estimated state may further include an absolute depth from airway to location of the tip of the instrument, as well as a set of data representing the set of branches traversed by the instrument within the tubular network, the set being a subset of the entire set of branches provided by the 3D model of the patient's lungs, for example. The application of probability distribution and confidence value on estimated states allows improved accuracy of estimation of location and/or orientation of the instrument tip within the tubular network.
- As shown in
FIG. 9B , the algorithm modules include an EM-basedalgorithm module 950, an image-basedalgorithm module 960, and a robot-basedalgorithm module 970. The algorithm modules shown inFIG. 9B is merely one example, and in alternative embodiments, different and/additional algorithm modules involving different and/or additional navigation algorithms can also be included in thenavigation module 905. - VI.B.2.i. Image-Based Algorithm Module
- Turning back to
FIG. 9B , the image-basedalgorithm module 960 uses image data to determine the estimated state of the instrument within the tubular network. The image-basedalgorithm module 960 further includes one or more different types of image-based algorithm modules that employ different image-based algorithms. As shown inFIG. 9B , one example including an object-basedalgorithm module 962 is shown. In alternative embodiments not shown, other types of image-based algorithms may be employed and corresponding algorithm modules may be included in the image-basedalgorithm module 960. In addition to determining the estimated state of the instrument, the image-basedalgorithm module 960 may also detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument and provide an indication of the detected change in location to thestate estimator 980. Further detail regarding aspects of the detection of the change in location of the instrument will be provided below in connection withFIG. 11 . - The object-based
algorithm module 962 detects and analyzes objects present in the field of view of the image data, such as branch openings or particles, to determine estimated state. In one embodiment, it includes anobject detection module 963, and objectmapping module 964, atopological reasoning module 965, and amotion estimation module 966. In some embodiments, it may or may not be necessary to apply thedifferent modules algorithm module 962, the order of employing each module within themodule 962 is a different order than shown inFIG. 9B . - Turning to
FIG. 10A , themotion estimation module 963 receives as inputs image data from theimage data store 910, estimated state data (prior) (specifically bifurcation data), from the estimatedstate data store 985 as well as the 3D model data from the 3Dmodel data store 940. Based on the received image data, themotion estimation module 963 measures a movement of the medical instrument between multiple image frames based on the received image data. Example techniques used include optical flow and image registration techniques, among others. This measurement determines a differential movement, such as forward-backward motion or roll motion, of the instrument tip in its own local frame of reference. This movement can be combined with the prior estimated state input to calculate a new estimated state. In particular, a forward (or backward) movement can translate into an increase (or decrease) in depth relative to a prior estimated state. Similarly, a differential roll translates into a change in roll angle relative to a prior estimated state. These measurements allow an estimation of movement through the tubular network. As above, these estimations may be represented as a probability distribution (e.g., a roll angle of the medical instrument of 40±5 degrees represented by a Gaussian distribution). The output estimated state is stored in the estimatedstate data store 985. - In one embodiment, in a case where the estimated state and bifurcation data for a particular instant in time indicate that the instrument tip is at or near a branch point, this movement measurement may include an identification of an estimated new branch that the instrument tip is estimated to be entering or have entered. For example, if the bifurcation data indicates that the endoscope tip is at a branch point, pitch and yaw movements can be measured to determine changes in pointing angle, and the new estimated angle can be compared with the expected angles of different branches in the 3D model of the tubular network. A determination can then be made of which branch the endoscope is facing towards when it is moved into a new branch. Estimated state data reflecting each of these estimates of new position, orientation, and/or branch entry are output to the
state estimator 980. -
FIG. 10B shows an example block diagram of theobject detection module 964, according to one example. Theobject detection module 964 receives as inputs image data (e.g., image frames), and outputs object data to anobject data store 1063 as well as estimated state data to the estimatedstate data store 985. Object data indicates information about what objects were identified, as well as positions, orientations, and sizes of objects represented as probabilities. - Specifically, the
object detection module 964 detects, within an image, one or more objects and one or more points of interest of the object(s) that may indicate branch points in a tubular network, and then determines their position, size, and orientation. The objects detected by theobject detection module 964 may also be referred to as “points of interest,” which may include, for example, one or more identifiable pixels within an image. In certain embodiments, the points of interest may comprise a set of one or more pixels which can be detected over a sequence of one or more images. In some implementations, the detected object may comprise one of more points of interest (e.g., image features) detected using image processing techniques of the related art, such as speeded up robust features (SURF) and scale-invariant feature transform (SIFT). However, any technique which can reliably detect and track one or more pixels through a series of images can be used to detect the points of interest which can be used in the image processing techniques described herein. - In certain implementations, objects may be calculated or represented in the
object detection module 964 as being two-dimensional shapes, such as circles/ovals/ellipses for detected branch points. This corresponds to the fact that the image data used to capture the objects are images from the camera on the instrument tip pointed usually along an axis substantially parallel to the direction of the segment in which the instrument is located. As a consequence, objects such as branches in the tubular network appear as simple shapes such as ellipses in the images. In one embodiment, in a given image within a tubular network, each branch will typically appear as a dark, approximately elliptical region, and these regions may be detected automatically by a processor, using region-detection algorithms such as maximally stable extremal regions (MSER) as objects. These regions may then be fit to define an object (e.g., ellipse), with appropriate free parameters such as ellipse center, major and minor axes, and angle within the image. The roll measurement and the identified matching between model lumens and lumens in the image are also output to thestate estimator 980, as well astopological reasoning module 966. An example of identified objects superimposed on an image of a bronchial network, along with a link joining their centers, is described with reference toFIGS. 11A-11B . - In one embodiment, an “airway” can also be identified as an object present in the image data. The
object detection module 964 may use light reflective intensity combined with other techniques to identify airways. - The
object detection module 964 may further track detected objects or points of interest across a set of sequential image frames. The object tracking may be used to detect which branch has been entered from among a set of possible branches in the tubular network. Alternatively or in addition, the tracking of objects may be used to detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument as described in greater detail below. Tracking the relative positions of the objects within the image frames may also be used to determine a local, absolute measurement of roll angle within a branched network. -
FIG. 10C shows an example block diagram of theobject mapping module 965, according to one embodiment. Theobject mapping module 965 receives asinputs 3D model data from the 3Dmodel data store 940, object data (e.g., detected objects such as shapes representing possible branches in the tubular network) from theobject data store 1063, and estimated state data (prior) from the estimatedstate data store 985. - Based on the received input data, the
object mapping module 965 outputs object mapping data to an objectmapping data store 1065 as well as image-based estimated state data (current) to the estimatedstate data store 985. As one example, the object mapping data indicates mapping information between physical branches (lumens) shown in image data (based on the detected objects) and virtual branch information generated by 3D model. The estimated state data (current) generated bymodule 965 includes identification of each branch of the tubular network visible within the image as well as an estimate of the roll of the endoscope tip relative to the 3D model. As above, the estimated state data (current) can be represented as a probability distribution. The identification of the visible lumens may include coordinates in x and y of each identified lumen center within the image, for example based on object sizes correlated with the 3D model virtual image data, as well as an association of each identified lumen location with a particular branch of the tubular network. - In some embodiments, since the 3D model is generated prior to the endoscopic procedure, the virtual images of the tubular network may be pre-computed to speed up processing. In alternative embodiments not shown, the tubular network may be represented by a structure such as a tree diagram of lumen midlines, with each such midline describing a 3D path, so that an expected position of local branch centers as seen from any arbitrary perspective may be compared to the identified actual locations of branch centers based on EM data and/or robot data.
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FIGS. 11A-11B , show an example object-to-lumen mapping performed by theobject mapping module 965, according to one embodiment. More specifically,FIG. 11A shows two example identifiedobjects bronchial network 1105 along with alink 1103 connecting centers of the two objects, according to one embodiment. In the illustrated example, the identifiedobjects -
FIG. 11B shows a matching between airway lumens in anactual image 1110 of a real bronchial network and a correspondingvirtual image 1120 from a 3D model of that same network, according to one embodiment. In theactual image 1110, ellipses are identified corresponding to two different branches, located with identifiedcenters FIGS. 6A-6B . The 3D modelvirtual image 1120 is a simulated representation of the real bronchial network shown in theactual image 1110, and the estimatedcenters state estimator 980 are shown corresponding to the positions of the identifiedcenters - If both
images 3D model image 1120 may be rotated or translated to increase the closeness of fit betweenactual image 1110 andvirtual image 1120, and the amount of roll needed for the rotation or translation can be output as a correction to the current estimated state (e.g., roll of the instrument tip). - In one embodiment, the probability applied to a possible estimated state as generated by the
object mapping module 965 is based on the closeness of fit between the identifiedcenters actual image 1110 and estimatedcenters 3D model image 1120, and as one example, the probability of being in the lumen with identifiedcenter 1112 drops as the distance between the estimatedcenter 1122 and identifiedcenter 1112 increases. -
FIG. 10D shows an example block diagram of thetopological reasoning module 966, according to one embodiment. Thetopological reasoning module 966 receives as input image data from the 3D model data from the 3Dmodel data store 940, object mapping data from the objectmapping data store 1065, and estimated state data (prior) from the estimatedstate data store 985. - Based on the received data, the
topological reasoning module 966 determines which branch the endoscope tip is facing towards, thereby generating a prediction of which branch will be entered if the endoscope is moved forward. As above, the determination may be represented as a probability distribution. In one embodiment, when the instrument tip is moving forward, thetopological reasoning module 966 determines that a new branch of the tubular network has been entered and identifies which branch the tip has moved into. The determination of which branch is being faced and which segment is entered may be made, for example, by comparing the relative sizes and locations of different identified objects (e.g., ellipses). As one example, as a particular lumen branch is entered, a corresponding detected object will grow larger in successive image frames, and will also become more centered in those frames. If this is behavior is identified for one of the objects, thetopological reasoning module 966 assigns an increasingly large probability to a corresponding estimated state as the endoscope tip moves towards the lumen associated with that object. Other branches are assigned correspondingly lower probabilities, until finally their object shapes disappear from images entirely. In one embodiment, the probability of the medical instrument being in those branches depends only on the probability that the branches were misidentified by theobject mapping module 964. The output of thetopological reasoning module 966 is image-based estimated state data representing estimated probabilities of being in each of a set of possible branches within the branched network. - Pulmonologists can prevent intra-operative trauma by basing their decisions and actions on the respiratory cycle of the patient. One example of such an action is insertion of a biopsy tool to collect tissue samples, for example via bronchoscopy. At or near the periphery of the lung the airways may be narrow, and the circumference of the airways changes depending on the respiratory phase of the lung. The diameter of an airway expands as a patient inhales in the inspiration phase of the respiratory cycles and constricts as the patient exhales during the expiration phase of the cycle. During a procedure, a pulmonologist can observe the patient to determine whether they are in the inspiration phase or the expiration phase in order to decide whether a particular tool or endoscope of fixed diameter can enter the airway. An airway can close around a tool during expiration without causing trauma, however forcing a tool through a constricted airway during the expiration phase can cause critical trauma, for example by puncturing a blood vessel.
- The aforementioned problems, among others, are addressed in certain embodiments by the luminal network navigation systems and techniques described herein. Some embodiments of the disclosed luminal network navigation systems and techniques relate to incorporating respiratory frequency and/or magnitude into a navigation framework to implement patient safety measures (e.g., instrument control techniques, user interface alerts, notifications, and the like).
- A patient's respiratory cycle may also affect the accuracy of the detection of the position and/or orientation of an instrument inserted into the patient's airways. Thus, some embodiments of the disclosed bronchoscopy navigation systems and techniques relate to identifying, and/or compensating for, motion caused by patient respiration in order to provide a more accurate identification of the position of an instrument within patient airways. For example, an instrument positioned within patient airways can be provided with an EM sensor. The navigation system can filter instrument position information from the EM sensor to remove signal noise due to cyclic motion of the respiratory passages caused by respiration. A frequency of the cyclic respiratory motion can be obtained from data from one or more additional sensors. In some implementations, inspiration and expiration cycles can be determined based on data from additional EM sensor(s), accelerometer(s), and/or acoustic respiratory sensor(s) placed on the body of the patient in one example. In some implementations, the frequency can be obtained from other types of sensors or systems, for example respiratory cycle information from a ventilator used to control patient breathing, or respiratory cycle information extracted from automated analysis of images received from an optical sensor positioned to observe the patient.
- Under certain circumstances, the filtering of the patient's respiration from the position information received from the EM sensor may not be sufficient to determine a sufficiently accurate estimate of the position of the instrument. For example, when additional EM sensor(s) are placed on the exterior of a patient's body, the EM sensors may detect the motion due to respiration in a transverse direction. That is, the EM sensor may track the overall expansion and contraction of the patient's airway via the movement of the EM sensors placed on the body of the patient.
- Depending on the location of the instrument within the patient's airway, the patient's respiration may also have another effect on the location of the instrument. That is, the length of the path traversed by the instrument within the luminal network may expand and contract along with the respiratory cycle. Since the length of the instrument may not appreciably change during the procedure, the relative position of the instrument with respect to the luminal network may change as the overall length of luminal network defined by the path taken by the instrument in the luminal network expands and contracts. From the reference point of the distal end of the instrument, this may appear as though the instrument is being advanced and retraced within the luminal network even though the instrument is not being actively driven. In certain circumstances, the instrument may be substantially stationary with respect to the reference point of the platform even while from the reference point of the distal end of the instrument, the instrument is being advanced and retraced. In this case, the location of the instrument determined based on the EM sensor may indicate that the instrument is substantially stationary, the location of the instrument with respect to the reference frame of the luminal network may be changing in accordance with the patient's respiratory cycle.
- Thus, certain aspects of this disclosure may relate to the detection of movement of the instrument with respect to the reference frame of the luminal network (e.g., movement of the luminal network around the instrument) due to a patient's respiration (or other physiological motion). Once detected, the robotic system may provide a user interface alert to indicate that there may be a certain amount of uncompensated error in the displayed location of the instrument.
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FIG. 12 is a flowchart illustrating an example method operable by a robotic system, or component(s) thereof, for detecting physiological noise in accordance with aspects of this disclosure. For example, the steps ofmethod 1200 illustrated inFIG. 12 may be performed by processor(s) and/or other component(s) of a medical robotic system (e.g., surgical robotic system 500) or associated system(s) (e.g., the image-basedalgorithm module 960 of the navigation configuration system 900). For convenience, themethod 1200 is described as performed by the navigation configuration system, also referred to simply as the “system” in connection with the description of themethod 1200. - The
method 1200 begins atblock 1201. Atblock 1205, the system may receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient. In example embodiments, the instrument may comprise a bronchoscope configured to be driven through a patient's airways. In these embodiments, the system may be configured to detect respiratory motion of the instrument based at least in part on the images received from the image sensor. - At
block 1210, the system may detect a set of one or more points of interest the first image data. As discussed above, the points of interest may be any distinguishable data across multiple image data, such as, for example, one or more identifiable pixels within the image or one or more objects detected in the image data. In certain embodiments, the points of interest may comprise a set of one or more pixels which can be detected over a sequence of one or more images. In some implementations, the detected object may comprise one of more distinguishable objects detected using image processing techniques of the related art, such as SURF and SIFT. However, any technique which can reliably detect and track one or more pixels through a series of images can be used to detect the points of interest which can be used in the image processing techniques described herein. - At
block 1215, the system may identify a set of first locations respectively corresponding to the set of points in the first image data. In embodiments where the set of points correspond to identifiable pixels within the image, the set of locations may correspond to the row and/or column values of the pixels within the image. Thus, the first set of locations may comprise the X- and Y-coordinates for each of the pixels in the set of points. - At
block 1220, the system may receive second image data from the image sensor. The second image may be an image received from the image sensor at a point in time occurring after the time at which the first image was captured by the image sensor. - At
block 1225, the system may detect the set of one or more points in the second image data. The set of points detected in the second image may correspond to the set of points detected in the first image. The detection of the same set of points between multiple images will be described in greater detail below in connection withFIGS. 13A-13C . - At
block 1230, the system may identify a set of second locations respectively corresponding to the set of points in the second image data. In some situations, when the physiological noise is affecting the relative position of the instrument with respect to the luminal network, the set of locations of the points of interest in the second image may be different from the set of locations of the points of interest in the first image. For example, as the instrument is advanced into the luminal network (e.g., due to contraction of the length of the luminal network), an object appearing in the images captured by the image sensor may appear as though it is approaching the image sensor. Thus, by tracking the location of the object (e.g., by tracking the points of interest), the system may be able to estimate motion of the instrument with respect to the luminal network. - At
block 1235, the system may, based on the set of first locations and the set of second locations, detect a change of location of the luminal network around the instrument caused by movement of the luminal network relative to the instrument. As described above, movement of the location of the tracked points of interest may be indicative of movement of the luminal network with respect to the instrument. Specific embodiments related to the detection of the change of location caused by movement of the luminal network will be described below in connection withFIGS. 14A-15B . Themethod 1200 ends atblock 1240. -
FIG. 13A illustrates example image data captured by an image sensor at a first point in time in accordance with aspects of this disclosure.FIG. 13B illustrates another example of image data captured by an image sensor at a second point in time, after the first point in time, in accordance with aspects of this disclosure. Depending on the embodiment, the first image data and the second image data may be successive images in a series of image data frames captured by the image sensor or may be separated in time with at least one additional image data frame interposed therebetween.FIG. 13C illustrates an example of the change in location of example pixels between the image data frames illustrated inFIGS. 13A-13B in accordance with aspects of this disclosure.FIG. 13D illustrates another example of the change in location of example pixels between the image data frames illustrated inFIGS. 13A-13B in accordance with aspects of this disclosure. - The image data frames illustrated in
FIGS. 13A-13B are simplified to show certain aspects of the detected image data which may be involved in the tracking of the locations of points of interest between a series of image data frames. In certain embodiments, the image data captured by an image sensor of an instrument may include an array of pixels having a greater or lesser number of pixels than illustrated. For example, the image sensor may be configured to capture 200×200 pixel image data frames in certain implementations. -
FIG. 13A illustratesfirst image data 1300A including two points ofinterest interest interest first image data 1300A. Further, in theFIG. 13A example, the points ofinterest first image data 1300A. However, as discussed above, in other embodiments the points ofinterest object detection module 964 or points of interest detected using image processing techniques such as SURF and/or SIFT. - In the
second image data 1300B ofFIG. 13B , the system may detect the same points ofinterest first image data 1300A. However, the points ofinterest second image data 1300B in the time elapsed between the capturing of the first andsecond image data interest first image data 1300A to their respective locations in thesecond image data 1300B may be based on the relative movement of the corresponding portions of the luminal network with respect to the location of the image sensor. When the instrument is stationary with respect to the robotic system (e.g., when no robotic commands are being provided to drive the instrument), the system may be able to infer that movement of the points ofinterest - The locations of the points of interest in
FIGS. 13A-14B may include information 911 (e.g., seeFIG. 10A ) the 2D locations of the points within the first image data and the second image data. Thus, the locations of the points of interest may include the X- and Y-coordinates for each of the points. In other embodiments, the system may track information 911 such as the location of the points in 3D space (not illustrated) based on theimage data image data -
FIG. 13C illustrates the locations of the points of interest at each of the first point in time and the second point in time overlaid on the same image data frame. As shown inFIG. 13C , a first point ofinterest first image data 1300A to different location in thesecond image data 1300B. The movement of the first point of interest is illustrated by thevector 1315. Similarly, a second point ofinterest second image data vector 1325. - In more general terms, the system may track a set of points of interest over a series of image data frames received from an image sensor positions on the instrument. The system may determine a “scale change” between two successive image data frames in the series.
FIG. 13D illustrates another example of the locations of the points of interest at each of the first point in time and the second point in time overlaid on the same image data to illustrate the relative distances between the points of interest. As shown inFIG. 13D , a first point ofinterest first image data 1300A to different location in thesecond image data 1300B. The system may determine a determine afirst distance 1330 between thefirst point 1310A and thesecond point 1320A in the first image data based on the locations of thefirst point 1310A and thesecond point 1320A in thefirst image data 1300A. The system may also determine asecond distance 1335 between thefirst point 1310B and thesecond point 1320B in thesecond image data 1300B based on the locations of thefirst point 1310B and thesecond point 1320B in thefirst image data 1300B. In some implementations, the first andsecond distances - The system may use the
first distance 1330 and thesecond distance 1335 to detect the change of location of the instrument within the luminal network. For example, in one embodiment, the system may determine a scale change estimate based on thefirst distance 1330 and thesecond distance 1335. In one implementation, the scale change estimate may be based on the difference between thefirst distance 1330 and thesecond distance 1335. - Although only two points of interest are illustrated in
FIGS. 13A-13D , the system may track a set of at least three points of interest over the series of image data frames. When the number of points in the set of points of interest is less than the number of pixels in the image data, the set of points may be considered a “sparse” set of points. In other embodiments, the number of points in the set of points of interest tracked by the system may be a “dense” set of points, where the number of tracked points is equal to the number of pixels in the image data. The system may group the points in the set of points a plurality of pairs of points. This may include each combination of pairs of points for the entire set of points or may include a subset of the possible pairs of points for the set of points. - The system may determine a scale change value between the two image data frames based on the scale estimates determined for the pairs of points. In certain embodiments, the scale change value may be representative of the scale change between the two image data frames based on all or a subset of the tracked pairs of points. In one embodiment, the system may determine the scale change value as a median value of the scale change estimates. In another embodiment, the system may determine the scale change value as an average value of the scale change estimates. Those skilled in the art will recognize that other techniques or methodologies may be used to generate a scale change value based on the set of scale change estimates.
- The system may accumulate a scale change value over a sequence of image data frames, and there by track the scale change over more than two image data frames. In certain implementations, the system may accumulate the scale change value my multiplying the scale change values between successive pairs of image data frames in the sequence of image data frames.
FIGS. 14A-14B illustrate an example of two image data frames within a sequence of image data frames for which the scale change value may be accumulated in accordance with aspects of this disclosure.FIGS. 15A-15B are graphs which illustrate the changes to an accumulated scale change value over a sequence of image data frames in accordance with aspects of this disclosure. - With reference to
FIGS. 14A-15B , a sequence of image data is illustrated over a numbered sequence of image data frames, whereFIG. 15A includes image data from frame #930 to frame #1125 andFIG. 15B includes image data from frame #965 to frame #1155.FIG. 14A includesimage data 1405 from frame #1125 whileFIG. 14B includesimage data 1410 from frame #1155. - Each of
FIGS. 15A-15B illustrates cumulative scale values determined in accordance with aspects of this disclosure. For example, the values at each frame in the graphs may be calculated by multiplying a currently determined scale change value between two image data frames with the accumulated scale change value determined for the previous frame. As is shown the graphs, the cumulative scale change values are period when periodic physiological noise is affecting the position of the image sensor (and thus the distal end of the instrument). The system may track cumulative changes to the scale change value in the sequence of image data received from the image sensor over a first time period and determine the frequency of the physiological noise based on the cumulative scale change values over a period of time. In one embodiment, the system may transform the tracked scale change value into a frequency domain (e.g., using a Fourier or other transform). The system may further identify at least one harmonic in the tracked scale change value in frequency domain. In certain embodiments, the system may identify the first harmonic in the tracked scale change value in frequency domain as an estimated frequency of the physiological noise. - The frequency determined from the cumulative scale change values may be utilized as an estimate of the frequency of physiological noise. However, physiological noise may not always have a large enough effect on the location of the instrument with respect to the luminal network that the physiological noise will introduce error in the localization of the instrument (e.g., as determined by the navigation configuration system 900). Thus, in certain embodiments, the system may compare the estimated frequency of the physiological noise to a separately estimated frequency of the physiological noise.
- In one embodiment, the system may determine a first physiological movement frequency of the patient based on a sequence of image data frames received from the image sensor. The system may further determine a second physiological movement frequency of the patient based on the data received from one or more location sensors (e.g., an EM sensor, a shape-sensing fiber, robot command data, and a radiation-based image sensors). Examples of systems and techniques for determining a physiological movement frequency of a patient based on data received from one or more location sensors is described in U.S. Patent Application Pub. No. 2018/0279852, filed on Mar. 29, 2018, the entirety of which is incorporated herein by reference.
- The system may then determine whether the difference between the first physiological movement frequency based on the sequence of image data and the second physiological movement frequency based on the location sensor data is less than a threshold difference. When the difference between the first and second physiological movement frequencies is less than the threshold difference, the system may determine that the frequency of the scale changes in the sequence of image data frames is due to a physiological noise source. In certain embodiments, the system may provide an indication of the detected change of location of the instrument within the luminal network to a display in response to determining that the frequency of the scale changes in the sequence of image data frames is due to a physiological noise source.
- In contrast, when the difference between the first and second physiological movement frequencies is not less than the threshold difference, the system may not have sufficient confidence to determine that the movement in the luminal network with respect to the instrument will affect the accuracy of the in the localization of the instrument (e.g., as determined by the navigation configuration system 900). In other words, when the frequency of the scale changes in the sequence of image data does not sufficiently match the physiological frequency measured using a separate technique, the system may infer that the location of the instrument sufficiently stable with respect to the luminal network so as to not introduce errors into the localization of the instrument.
- Depending on the particular image processing technique used to identify the points of interest (e.g., SURF, SIFT, etc.), the order in which two frames of image data are processed may affect the identification of the locations of the points of interest within the image data. For example, referring to
FIGS. 13A-13B , in one example, the system may identifypixels image data frame 1300B by tracking a change in the location ofpixels frame 1300A. However, under certain conditions, in reversing this process by backtrackingpixels image data frame 1300B to imagedata frame 1300A, the system may identify different pixels from theoriginal pixels original pixels - Accordingly, the system may identify a set of backtracked locations of the set of points in first image data via backtracking the set of points from second image data to the first image data and compare the set of backtracked locations to the original set of locations of the set of points identified from the first image data. The system may identify a sub-set of the points from the set of points for which the backtracked locations are not within a threshold distance of the set of first locations (e.g., the locations of the backtracked pixels do not sufficiently match the originally determined locations of the pixels used for forward tracking). The system may remove the sub-set of points from the set of points and determine the scale change estimate without the removed sub-set of points. This may improve the accuracy and robustness of the point tracking over a series of image data frames.
- While certain aspects of this disclosure may be performed while the instrument is stationary (e.g., while no robot commands are provided to move the instrument), it may also be desirable to detect physiological noise during dynamic instrument movement (e.g., while driving the instrument within the luminal network). During such dynamic instrument movement, the change between two image data frames (e.g., received at a first time and a second time), may be the result of a combination of instrument movement and physiological noise. Accordingly, to detect the physiological noise during dynamic movement of the instrument, the instrument movement motion should be decoupled from the physiological noise in the motion detected by the image-based
algorithm module 970. In certain embodiments, the system can perform motion decoupling in 3D space by using the 3D movement of the instrument received from positioning sensors (e.g., EM-based state data, robot-based state data, EM and/or optical shape sensing state data, etc.). The system can employ certain image processing techniques including image-based 3D motion estimation (e.g., structure from motion) to determine the relative 3D motion between the instrument and the luminal network. - In one example implementation, the system may determine a location sensor-based 3D instrument movement between two points in time (e.g., between t0 and t1) based on data received from locations sensors. The 3D instrument movement data may be represented by three spatial degrees-of-freedom (DoF), for example {xz, yz, zz}, and three rotational DoF, for example, {θs x, θs y, θx z}. The system may also determine an image sensor-based 3D instrument movement between the two points in time represented by the same six DoF measurements as in the location sensor-based 3D instrument movement.
- The system may determine a 3D instrument movement estimate representative of physiological movement by determining the difference between the location sensor-based 3D instrument movement and the image sensor-based 3D instrument movement. The system may then accumulate the 3D instrument movement estimate representative of physiological movement over a sequence of image data and location sensor measurements over a given time period, from which a frequency and amplitude associated with the physiological noise can be extracted (e.g., using one or more of the above-defined techniques including harmonic analysis).
- There may be uncertainty in the determined location of an instrument introduced due to physiological noise when the instrument is located near a junction in a luminal network (e.g., when a current segment branches into two or more child segments). That is, if the instrument is not located near a junction, even though the instrument may have a change in depth within a current segment due to the physiological noise, the instrument may remain within the current segment without transitioning into another segment. However, if the instrument is located near a junction, the movement of the instrument due to physiological noise may be sufficient to move the instrument from one segment into another segment. Thus, it may be desirable to provide an indication to the user that the system is unable to accurately determine whether the instrument has crossed over the junction into a new segment of the luminal network.
- In certain embodiments, the system can detect junction transition by identifying and analyzing the airways from image data received from an image sensor. For example, the system tracks the locations of detected airways between two image data frames (e.g., received at a first time t0 and a second time t1). The system may, in certain embodiments, determine that the instrument has transitioned through a junction in response to at least one of the following conditions being satisfied: 1) all of the estimated airways overlap with the detected airways in the image data at time but there exists one or more detected airways in the image data at time t0 that do not have an overlap with the estimated airways; 2) all the detected airways in in the image data at time t1 overlap with the estimated airways, but there exists one or more estimated airways do not have an overlap with the detected airways the image data at time t0; and 3) there exists one or more detected airways that do not overlap with the estimated airways and there exists one or more estimated airways do not overlap with the detected airways. As such, embodiments may track the location and sizes of prior airways and compare them to the locations and sizes of airways detected in current image data. The presences of one or more of the above listed conditions and the detected of movement of the anatomy relative to the instrument may be used by the system to detect a transition between junctions.
- Implementations disclosed herein provide systems, methods and apparatuses for detecting physiological noise during navigation of a luminal network.
- It should be noted that the terms “couple,” “coupling,” “coupled” or other variations of the word couple as used herein may indicate either an indirect connection or a direct connection. For example, if a first component is “coupled” to a second component, the first component may be either indirectly connected to the second component via another component or directly connected to the second component.
- The path-based navigational functions described herein may be stored as one or more instructions on a processor-readable or computer-readable medium. The term “computer-readable medium” refers to any available medium that can be accessed by a computer or processor. By way of example, and not limitation, such a medium may comprise random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc read-only memory (CD-ROM) or other optical disk storage may comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. It should be noted that a computer-readable medium may be tangible and non-transitory. As used herein, the term “code” may refer to software, instructions, code or data that is/are executable by a computing device or processor.
- The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and/or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is required for proper operation of the method that is being described, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims.
- As used herein, the term “plurality” denotes two or more. For example, a plurality of components indicates two or more components. The term “determining” encompasses a wide variety of actions and, therefore, “determining” can include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” can include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” can include resolving, selecting, choosing, establishing and the like.
- The phrase “based on” does not mean “based only on,” unless expressly specified otherwise. In other words, the phrase “based on” describes both “based only on” and “based at least on.”
- The previous description of the disclosed implementations is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these implementations will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the scope of the invention. For example, it will be appreciated that one of ordinary skill in the art will be able to employ a number corresponding alternative and equivalent structural details, such as equivalent ways of fastening, mounting, coupling, or engaging tool components, equivalent mechanisms for producing particular actuation motions, and equivalent mechanisms for delivering electrical energy. Thus, the present invention is not intended to be limited to the implementations shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (20)
1. A medical robotic system, comprising:
a set of one or more processors; and
at least one computer-readable memory in communication with the set of processors and having stored thereon computer-executable instructions to cause the set of processors to:
receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient,
detect a set of one or more points of interest the first image data,
identify a set of first locations respectively corresponding to the set of points in the first image data,
receive second image data from the image sensor,
detect the set of one or more points in the second image data,
identify a set of second locations respectively corresponding to the set of points in the second image data, and
based on the set of first locations and the set of second locations, detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
2. The system of claim 1 , wherein:
the set of first locations and the set of second locations respectively define two-dimensional (2D) locations of the points within the first image data and the second image data.
3. The system of claim 2 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
group the set of points into a plurality of pairs of points, a first pair of points comprising a first point and a second point,
determine a first distance between the first point and the second point in the first image data based on the set of first locations, and
determine a second distance between the first point and the second point in the second image data based on the set of second locations, wherein detecting the change of location of the instrument within the luminal network is further based on the first distance and the second distance.
4. The system of claim 3 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
determine a first scale change estimate for the first pair of points based on the first distance and the second distance, and
determine a scale change value representative of the scale change between the first image data and the second image data based on the scale change estimate,
wherein detecting the change of location of the instrument within the luminal network is further based on the scale change value.
5. The system of claim 4 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
determine a set of scale change estimates respectively corresponding to the pairs of points, and
determine the scale change value based on a median value of the set of scale change estimates or an average value of the set of scale change estimates.
6. The system of claim 1 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
track cumulative changes to a scale change value representative of a scale change in image data received from the image sensor over a first time period,
transform the tracked scale change value into a frequency domain, and
identify at least one harmonic in the tracked scale change value in frequency domain,
wherein detecting the change of location of the instrument within the luminal network is further based on the at least one harmonic.
7. The system of claim 1 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
determine a location of the instrument based on data received from one or more location sensors,
determine a first physiological movement frequency of the patient based on the set of first locations and the set of second locations, wherein detecting the change of location of the instrument within the luminal network is further based on the first physiological movement frequency, and
provide an indication of the detected change of location of the instrument within the luminal network to a display.
8. The system of claim 7 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
determine a second physiological movement frequency of the patient based on the data received from the one or more location sensors, and
determine that a difference between the first physiological movement frequency and the second physiological movement frequency is less than a threshold difference,
wherein detecting the change of location of the instrument within the luminal network is further in response to determining that the difference between the first physiological movement frequency and the second physiological movement frequency is less than the threshold difference.
9. The system of claim 7 , wherein the one or more location sensors comprise at least one of: an electromagnetic (EM) sensor, a shape-sensing fiber, robot command data, and a radiation-based image sensor.
10. The system of claim 7 , wherein the physiological movement comprises at least one of a respiration of the patient or a heart rate of the patient.
11. The system of claim 1 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
identify a set of backtracked locations of the set of points in the first image data via backtracking the set of points from the second image data to the first image data,
compare the set of backtracked locations to the set of first locations,
identify a sub-set of points in the set of points for which the backtracked locations are not within a threshold distance of the set of first locations, and remove the sub-set of points from the set of points.
12. The system of claim 1 , wherein the first set of locations and the second set of locations comprise two-dimensional (2D) information indicative of the respective locations of the points with respect to a coordinate system of the first image data and the second image data.
13. The system of claim 1 , wherein the memory further has stored thereon computer-executable instructions to cause the set of processors to:
extract depth information for the set of points from the first image data, and
extract depth information for the set of points from the second image data,
wherein the first set of locations and the second set of locations comprise three-dimensional (3D) information indicative of the respective locations of the points determined based on depth information extracted from each of the first image data and second image data.
14. A non-transitory computer readable storage medium having stored thereon instructions that, when executed, cause at least one computing device to:
receive first image data from an image sensor located on an instrument, the instrument configured to be driven through a luminal network of a patient;
detect a set of one or more points of interest the first image data;
identify a set of first locations respectively corresponding to the set of points in the first image data;
receive second image data from the image sensor;
detect the set of one or more points in the second image data;
identify a set of second locations respectively corresponding to the set of points in the second image data; and
based on the set of first locations and the set of second locations, detect a change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
15. The non-transitory computer readable storage medium of claim 14 , wherein:
the set of first locations and the set of second locations respectively define two-dimensional (2D) locations of the points within the first image data and the second image data.
16. The non-transitory computer readable storage medium of claim 15 , wherein the non-transitory computer readable storage medium further has stored thereon instructions that, when executed, cause the at least one computing device to:
group the set of points into a plurality of pairs of points, a first pair of points comprising a first point and a second point;
determine a first distance between the first point and the second point in the first image data based on the set of first locations; and
determine a second distance between the first point and the second point in the second image data based on the set of second locations,
wherein detecting the change of location of the instrument within the luminal network is further based on the first distance and the second distance.
17. The non-transitory computer readable storage medium of claim 16 , wherein the non-transitory computer readable storage medium further has stored thereon instructions that, when executed, cause the at least one computing device to:
determine a first scale change estimate for the first pair of points based on the first distance and the second distance; and
determine a scale change value representative of the scale change between the first image data and the second image data based on the scale change estimate,
wherein detecting the change of location of the instrument within the luminal network is further based on the scale change value.
18. The non-transitory computer readable storage medium of claim 17 , wherein the non-transitory computer readable storage medium further has stored thereon instructions that, when executed, cause the at least one computing device to:
determine a set of scale change estimates respectively corresponding to the pairs of points; and
determine the scale change value based on a median value of the set of scale change estimates or an average value of the set of scale change estimates.
19. The non-transitory computer readable storage medium of claim 14 , wherein the non-transitory computer readable storage medium further has stored thereon instructions that, when executed, cause the at least one computing device to:
track cumulative changes to a scale change value representative of a scale change in image data received from the image sensor over a first time period;
transform the tracked scale change value into a frequency domain; and
identify at least one harmonic in the tracked scale change value in frequency domain, wherein detecting the change of location of the instrument within the luminal network is further based on the at least one harmonic.
20. A method for detecting a change of location of an instrument, comprising:
receiving first image data from an image sensor located on the instrument, the instrument configured to be driven through a luminal network of a patient;
detecting a set of one or more points of interest the first image data;
identifying a set of first locations respectively corresponding to the set of points in the first image data;
receiving second image data from the image sensor;
detecting the set of one or more points in the second image data;
identifying a set of second locations respectively corresponding to the set of points in the second image data; and
based on the set of first locations and the set of second locations, detecting the change of location of the instrument within the luminal network caused by movement of the luminal network relative to the instrument.
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Families Citing this family (151)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8414505B1 (en) | 2001-02-15 | 2013-04-09 | Hansen Medical, Inc. | Catheter driver system |
EP2384715B1 (en) | 2004-03-05 | 2015-07-08 | Hansen Medical, Inc. | Robotic catheter system |
US9232959B2 (en) | 2007-01-02 | 2016-01-12 | Aquabeam, Llc | Multi fluid tissue resection methods and devices |
US8814921B2 (en) | 2008-03-06 | 2014-08-26 | Aquabeam Llc | Tissue ablation and cautery with optical energy carried in fluid stream |
US9254123B2 (en) | 2009-04-29 | 2016-02-09 | Hansen Medical, Inc. | Flexible and steerable elongate instruments with shape control and support elements |
US8672837B2 (en) | 2010-06-24 | 2014-03-18 | Hansen Medical, Inc. | Methods and devices for controlling a shapeable medical device |
US20120191107A1 (en) | 2010-09-17 | 2012-07-26 | Tanner Neal A | Systems and methods for positioning an elongate member inside a body |
US9138166B2 (en) | 2011-07-29 | 2015-09-22 | Hansen Medical, Inc. | Apparatus and methods for fiber integration and registration |
EP2819599B1 (en) | 2012-02-29 | 2018-05-23 | Procept Biorobotics Corporation | Automated image-guided tissue resection and treatment |
US20130317519A1 (en) | 2012-05-25 | 2013-11-28 | Hansen Medical, Inc. | Low friction instrument driver interface for robotic systems |
US20140148673A1 (en) | 2012-11-28 | 2014-05-29 | Hansen Medical, Inc. | Method of anchoring pullwire directly articulatable region in catheter |
US10231867B2 (en) | 2013-01-18 | 2019-03-19 | Auris Health, Inc. | Method, apparatus and system for a water jet |
US10149720B2 (en) | 2013-03-08 | 2018-12-11 | Auris Health, Inc. | Method, apparatus, and a system for facilitating bending of an instrument in a surgical or medical robotic environment |
US9057600B2 (en) | 2013-03-13 | 2015-06-16 | Hansen Medical, Inc. | Reducing incremental measurement sensor error |
US9326822B2 (en) | 2013-03-14 | 2016-05-03 | Hansen Medical, Inc. | Active drives for robotic catheter manipulators |
US20140277334A1 (en) | 2013-03-14 | 2014-09-18 | Hansen Medical, Inc. | Active drives for robotic catheter manipulators |
US11213363B2 (en) | 2013-03-14 | 2022-01-04 | Auris Health, Inc. | Catheter tension sensing |
US9173713B2 (en) | 2013-03-14 | 2015-11-03 | Hansen Medical, Inc. | Torque-based catheter articulation |
US9271663B2 (en) | 2013-03-15 | 2016-03-01 | Hansen Medical, Inc. | Flexible instrument localization from both remote and elongation sensors |
US20140276647A1 (en) | 2013-03-15 | 2014-09-18 | Hansen Medical, Inc. | Vascular remote catheter manipulator |
US20140276936A1 (en) | 2013-03-15 | 2014-09-18 | Hansen Medical, Inc. | Active drive mechanism for simultaneous rotation and translation |
US9629595B2 (en) | 2013-03-15 | 2017-04-25 | Hansen Medical, Inc. | Systems and methods for localizing, tracking and/or controlling medical instruments |
US9283046B2 (en) | 2013-03-15 | 2016-03-15 | Hansen Medical, Inc. | User interface for active drive apparatus with finite range of motion |
US9452018B2 (en) | 2013-03-15 | 2016-09-27 | Hansen Medical, Inc. | Rotational support for an elongate member |
US10849702B2 (en) | 2013-03-15 | 2020-12-01 | Auris Health, Inc. | User input devices for controlling manipulation of guidewires and catheters |
US9408669B2 (en) | 2013-03-15 | 2016-08-09 | Hansen Medical, Inc. | Active drive mechanism with finite range of motion |
US10376672B2 (en) | 2013-03-15 | 2019-08-13 | Auris Health, Inc. | Catheter insertion system and method of fabrication |
US9014851B2 (en) | 2013-03-15 | 2015-04-21 | Hansen Medical, Inc. | Systems and methods for tracking robotically controlled medical instruments |
US11020016B2 (en) | 2013-05-30 | 2021-06-01 | Auris Health, Inc. | System and method for displaying anatomy and devices on a movable display |
US10744035B2 (en) | 2013-06-11 | 2020-08-18 | Auris Health, Inc. | Methods for robotic assisted cataract surgery |
US10426661B2 (en) | 2013-08-13 | 2019-10-01 | Auris Health, Inc. | Method and apparatus for laser assisted cataract surgery |
EP3243476B1 (en) | 2014-03-24 | 2019-11-06 | Auris Health, Inc. | Systems and devices for catheter driving instinctiveness |
US10046140B2 (en) | 2014-04-21 | 2018-08-14 | Hansen Medical, Inc. | Devices, systems, and methods for controlling active drive systems |
US10569052B2 (en) | 2014-05-15 | 2020-02-25 | Auris Health, Inc. | Anti-buckling mechanisms for catheters |
US10792464B2 (en) | 2014-07-01 | 2020-10-06 | Auris Health, Inc. | Tool and method for using surgical endoscope with spiral lumens |
US9561083B2 (en) | 2014-07-01 | 2017-02-07 | Auris Surgical Robotics, Inc. | Articulating flexible endoscopic tool with roll capabilities |
US9744335B2 (en) | 2014-07-01 | 2017-08-29 | Auris Surgical Robotics, Inc. | Apparatuses and methods for monitoring tendons of steerable catheters |
CN107427327A (en) | 2014-09-30 | 2017-12-01 | 奥瑞斯外科手术机器人公司 | Configurable robotic surgical system with virtual track and soft endoscope |
US10499999B2 (en) | 2014-10-09 | 2019-12-10 | Auris Health, Inc. | Systems and methods for aligning an elongate member with an access site |
US10314463B2 (en) | 2014-10-24 | 2019-06-11 | Auris Health, Inc. | Automated endoscope calibration |
US11819636B2 (en) | 2015-03-30 | 2023-11-21 | Auris Health, Inc. | Endoscope pull wire electrical circuit |
US20160287279A1 (en) | 2015-04-01 | 2016-10-06 | Auris Surgical Robotics, Inc. | Microsurgical tool for robotic applications |
WO2016164824A1 (en) | 2015-04-09 | 2016-10-13 | Auris Surgical Robotics, Inc. | Surgical system with configurable rail-mounted mechanical arms |
WO2016187054A1 (en) | 2015-05-15 | 2016-11-24 | Auris Surgical Robotics, Inc. | Surgical robotics system |
KR102429651B1 (en) | 2015-09-09 | 2022-08-05 | 아우리스 헬스, 인크. | Instrument Device Manipulator for Surgical Robot System |
US9727963B2 (en) | 2015-09-18 | 2017-08-08 | Auris Surgical Robotics, Inc. | Navigation of tubular networks |
US9955986B2 (en) | 2015-10-30 | 2018-05-01 | Auris Surgical Robotics, Inc. | Basket apparatus |
US10231793B2 (en) | 2015-10-30 | 2019-03-19 | Auris Health, Inc. | Object removal through a percutaneous suction tube |
US9949749B2 (en) | 2015-10-30 | 2018-04-24 | Auris Surgical Robotics, Inc. | Object capture with a basket |
US10143526B2 (en) | 2015-11-30 | 2018-12-04 | Auris Health, Inc. | Robot-assisted driving systems and methods |
US10932861B2 (en) | 2016-01-14 | 2021-03-02 | Auris Health, Inc. | Electromagnetic tracking surgical system and method of controlling the same |
US10932691B2 (en) | 2016-01-26 | 2021-03-02 | Auris Health, Inc. | Surgical tools having electromagnetic tracking components |
US11324554B2 (en) | 2016-04-08 | 2022-05-10 | Auris Health, Inc. | Floating electromagnetic field generator system and method of controlling the same |
US10454347B2 (en) | 2016-04-29 | 2019-10-22 | Auris Health, Inc. | Compact height torque sensing articulation axis assembly |
US11037464B2 (en) | 2016-07-21 | 2021-06-15 | Auris Health, Inc. | System with emulator movement tracking for controlling medical devices |
US10463439B2 (en) | 2016-08-26 | 2019-11-05 | Auris Health, Inc. | Steerable catheter with shaft load distributions |
US11241559B2 (en) | 2016-08-29 | 2022-02-08 | Auris Health, Inc. | Active drive for guidewire manipulation |
KR102555546B1 (en) | 2016-08-31 | 2023-07-19 | 아우리스 헬스, 인코포레이티드 | length-preserving surgical instruments |
US9931025B1 (en) | 2016-09-30 | 2018-04-03 | Auris Surgical Robotics, Inc. | Automated calibration of endoscopes with pull wires |
US10244926B2 (en) | 2016-12-28 | 2019-04-02 | Auris Health, Inc. | Detecting endolumenal buckling of flexible instruments |
US10136959B2 (en) | 2016-12-28 | 2018-11-27 | Auris Health, Inc. | Endolumenal object sizing |
AU2018244318B2 (en) | 2017-03-28 | 2023-11-16 | Auris Health, Inc. | Shaft actuating handle |
WO2018183727A1 (en) | 2017-03-31 | 2018-10-04 | Auris Health, Inc. | Robotic systems for navigation of luminal networks that compensate for physiological noise |
KR20230106716A (en) | 2017-04-07 | 2023-07-13 | 아우리스 헬스, 인코포레이티드 | Patient introducer alignment |
US10285574B2 (en) | 2017-04-07 | 2019-05-14 | Auris Health, Inc. | Superelastic medical instrument |
CN110831498B (en) | 2017-05-12 | 2022-08-12 | 奥瑞斯健康公司 | Biopsy device and system |
JP7301750B2 (en) | 2017-05-17 | 2023-07-03 | オーリス ヘルス インコーポレイテッド | Interchangeable working channel |
US10022192B1 (en) | 2017-06-23 | 2018-07-17 | Auris Health, Inc. | Automatically-initialized robotic systems for navigation of luminal networks |
JP7317723B2 (en) | 2017-06-28 | 2023-07-31 | オーリス ヘルス インコーポレイテッド | Electromagnetic field distortion detection |
JP7330902B2 (en) | 2017-06-28 | 2023-08-22 | オーリス ヘルス インコーポレイテッド | Electromagnetic distortion detection |
US11026758B2 (en) | 2017-06-28 | 2021-06-08 | Auris Health, Inc. | Medical robotics systems implementing axis constraints during actuation of one or more motorized joints |
WO2019005872A1 (en) | 2017-06-28 | 2019-01-03 | Auris Health, Inc. | Instrument insertion compensation |
US10426559B2 (en) | 2017-06-30 | 2019-10-01 | Auris Health, Inc. | Systems and methods for medical instrument compression compensation |
US10464209B2 (en) | 2017-10-05 | 2019-11-05 | Auris Health, Inc. | Robotic system with indication of boundary for robotic arm |
US10145747B1 (en) | 2017-10-10 | 2018-12-04 | Auris Health, Inc. | Detection of undesirable forces on a surgical robotic arm |
US10016900B1 (en) | 2017-10-10 | 2018-07-10 | Auris Health, Inc. | Surgical robotic arm admittance control |
US11058493B2 (en) | 2017-10-13 | 2021-07-13 | Auris Health, Inc. | Robotic system configured for navigation path tracing |
US10555778B2 (en) | 2017-10-13 | 2020-02-11 | Auris Health, Inc. | Image-based branch detection and mapping for navigation |
KR102645922B1 (en) | 2017-12-06 | 2024-03-13 | 아우리스 헬스, 인코포레이티드 | Systems and methods for correcting non-directed instrument rolls |
JP7314136B2 (en) | 2017-12-08 | 2023-07-25 | オーリス ヘルス インコーポレイテッド | Systems and methods for navigation and targeting of medical instruments |
WO2019113389A1 (en) | 2017-12-08 | 2019-06-13 | Auris Health, Inc. | Directed fluidics |
CN111770736A (en) | 2017-12-11 | 2020-10-13 | 奥瑞斯健康公司 | System and method for instrument-based insertion architecture |
US11510736B2 (en) | 2017-12-14 | 2022-11-29 | Auris Health, Inc. | System and method for estimating instrument location |
CN110809453B (en) | 2017-12-18 | 2023-06-06 | 奥瑞斯健康公司 | Method and system for instrument tracking and navigation within a luminal network |
KR102264368B1 (en) | 2018-01-17 | 2021-06-17 | 아우리스 헬스, 인코포레이티드 | Surgical platform with adjustable arm support |
USD924410S1 (en) | 2018-01-17 | 2021-07-06 | Auris Health, Inc. | Instrument tower |
USD932628S1 (en) | 2018-01-17 | 2021-10-05 | Auris Health, Inc. | Instrument cart |
JP7463277B2 (en) | 2018-01-17 | 2024-04-08 | オーリス ヘルス インコーポレイテッド | Surgical robotic system having improved robotic arm |
USD901694S1 (en) | 2018-01-17 | 2020-11-10 | Auris Health, Inc. | Instrument handle |
USD873878S1 (en) | 2018-01-17 | 2020-01-28 | Auris Health, Inc. | Robotic arm |
USD901018S1 (en) | 2018-01-17 | 2020-11-03 | Auris Health, Inc. | Controller |
EP3752085A4 (en) | 2018-02-13 | 2021-11-24 | Auris Health, Inc. | System and method for driving medical instrument |
EP3773304A4 (en) | 2018-03-28 | 2021-12-22 | Auris Health, Inc. | Systems and methods for displaying estimated location of instrument |
CN110891469B (en) * | 2018-03-28 | 2023-01-13 | 奥瑞斯健康公司 | System and method for registration of positioning sensors |
US11109920B2 (en) | 2018-03-28 | 2021-09-07 | Auris Health, Inc. | Medical instruments with variable bending stiffness profiles |
EP3793465A4 (en) | 2018-05-18 | 2022-03-02 | Auris Health, Inc. | Controllers for robotically-enabled teleoperated systems |
US10905499B2 (en) | 2018-05-30 | 2021-02-02 | Auris Health, Inc. | Systems and methods for location sensor-based branch prediction |
CN110831481B (en) | 2018-05-31 | 2022-08-30 | 奥瑞斯健康公司 | Path-based navigation of tubular networks |
MX2020012904A (en) | 2018-05-31 | 2021-02-26 | Auris Health Inc | Image-based airway analysis and mapping. |
KR102567087B1 (en) | 2018-05-31 | 2023-08-17 | 아우리스 헬스, 인코포레이티드 | Robotic systems and methods for navigation of luminal networks detecting physiological noise |
MX2020013241A (en) | 2018-06-07 | 2021-02-22 | Auris Health Inc | Robotic medical systems with high force instruments. |
WO2020005348A1 (en) | 2018-06-27 | 2020-01-02 | Auris Health, Inc. | Alignment and attachment systems for medical instruments |
US11399905B2 (en) | 2018-06-28 | 2022-08-02 | Auris Health, Inc. | Medical systems incorporating pulley sharing |
CN112804946A (en) | 2018-08-07 | 2021-05-14 | 奥瑞斯健康公司 | Combining strain-based shape sensing with catheter control |
US10828118B2 (en) | 2018-08-15 | 2020-11-10 | Auris Health, Inc. | Medical instruments for tissue cauterization |
CN112566567A (en) | 2018-08-17 | 2021-03-26 | 奥瑞斯健康公司 | Bipolar medical instrument |
WO2020041619A2 (en) | 2018-08-24 | 2020-02-27 | Auris Health, Inc. | Manually and robotically controllable medical instruments |
CN112739283A (en) | 2018-09-17 | 2021-04-30 | 奥瑞斯健康公司 | System and method for accompanying medical procedure |
WO2020068303A1 (en) | 2018-09-26 | 2020-04-02 | Auris Health, Inc. | Systems and instruments for suction and irrigation |
CN112804933A (en) | 2018-09-26 | 2021-05-14 | 奥瑞斯健康公司 | Articulating medical device |
EP3856001A4 (en) | 2018-09-28 | 2022-06-22 | Auris Health, Inc. | Devices, systems, and methods for manually and robotically driving medical instruments |
KR20210073542A (en) | 2018-09-28 | 2021-06-18 | 아우리스 헬스, 인코포레이티드 | Systems and methods for docking medical instruments |
US11576738B2 (en) | 2018-10-08 | 2023-02-14 | Auris Health, Inc. | Systems and instruments for tissue sealing |
EP3866718A4 (en) | 2018-12-20 | 2022-07-20 | Auris Health, Inc. | Systems and methods for robotic arm alignment and docking |
WO2020131529A1 (en) | 2018-12-20 | 2020-06-25 | Auris Health, Inc. | Shielding for wristed instruments |
JP2022515835A (en) | 2018-12-28 | 2022-02-22 | オーリス ヘルス インコーポレイテッド | Percutaneous sheath for robotic medical systems and methods |
CN113347938A (en) | 2019-01-25 | 2021-09-03 | 奥瑞斯健康公司 | Vascular sealer with heating and cooling capabilities |
EP3890644A4 (en) | 2019-02-08 | 2022-11-16 | Auris Health, Inc. | Robotically controlled clot manipulation and removal |
EP3890645A4 (en) | 2019-02-22 | 2022-09-07 | Auris Health, Inc. | Surgical platform with motorized arms for adjustable arm supports |
US10945904B2 (en) | 2019-03-08 | 2021-03-16 | Auris Health, Inc. | Tilt mechanisms for medical systems and applications |
CN113613580A (en) | 2019-03-22 | 2021-11-05 | 奥瑞斯健康公司 | System and method for aligning inputs on a medical instrument |
WO2020197625A1 (en) | 2019-03-25 | 2020-10-01 | Auris Health, Inc. | Systems and methods for medical stapling |
US11617627B2 (en) | 2019-03-29 | 2023-04-04 | Auris Health, Inc. | Systems and methods for optical strain sensing in medical instruments |
EP3952779A4 (en) | 2019-04-08 | 2023-01-18 | Auris Health, Inc. | Systems, methods, and workflows for concomitant procedures |
US11369386B2 (en) | 2019-06-27 | 2022-06-28 | Auris Health, Inc. | Systems and methods for a medical clip applier |
EP3989863A4 (en) | 2019-06-28 | 2023-10-11 | Auris Health, Inc. | Medical instruments including wrists with hybrid redirect surfaces |
US11872007B2 (en) | 2019-06-28 | 2024-01-16 | Auris Health, Inc. | Console overlay and methods of using same |
JP2022544554A (en) | 2019-08-15 | 2022-10-19 | オーリス ヘルス インコーポレイテッド | Medical device with multiple bends |
US11896330B2 (en) | 2019-08-15 | 2024-02-13 | Auris Health, Inc. | Robotic medical system having multiple medical instruments |
WO2021038495A1 (en) | 2019-08-30 | 2021-03-04 | Auris Health, Inc. | Instrument image reliability systems and methods |
WO2021038469A1 (en) | 2019-08-30 | 2021-03-04 | Auris Health, Inc. | Systems and methods for weight-based registration of location sensors |
EP4025921A4 (en) | 2019-09-03 | 2023-09-06 | Auris Health, Inc. | Electromagnetic distortion detection and compensation |
US11234780B2 (en) | 2019-09-10 | 2022-02-01 | Auris Health, Inc. | Systems and methods for kinematic optimization with shared robotic degrees-of-freedom |
CN114502094A (en) | 2019-09-26 | 2022-05-13 | 奥瑞斯健康公司 | System and method for collision detection and avoidance |
US11737845B2 (en) | 2019-09-30 | 2023-08-29 | Auris Inc. | Medical instrument with a capstan |
US11737835B2 (en) | 2019-10-29 | 2023-08-29 | Auris Health, Inc. | Braid-reinforced insulation sheath |
KR20220144360A (en) * | 2019-12-19 | 2022-10-26 | 노아 메디컬 코퍼레이션 | Systems and methods for robotic bronchoscopy navigation |
JP2023507171A (en) * | 2019-12-19 | 2023-02-21 | ノア メディカル コーポレーション | Systems and methods for modular endoscopy |
CN114901188A (en) | 2019-12-31 | 2022-08-12 | 奥瑞斯健康公司 | Dynamic pulley system |
EP4084724A4 (en) | 2019-12-31 | 2023-12-27 | Auris Health, Inc. | Advanced basket drive mode |
CN114901194A (en) | 2019-12-31 | 2022-08-12 | 奥瑞斯健康公司 | Anatomical feature identification and targeting |
WO2021137108A1 (en) | 2019-12-31 | 2021-07-08 | Auris Health, Inc. | Alignment interfaces for percutaneous access |
CN114901192A (en) | 2019-12-31 | 2022-08-12 | 奥瑞斯健康公司 | Alignment technique for percutaneous access |
US20210369373A1 (en) * | 2020-05-28 | 2021-12-02 | The Chinese University Of Hong Kong | Mobile-electromagnetic coil-based magnetic actuation systems |
US11839969B2 (en) | 2020-06-29 | 2023-12-12 | Auris Health, Inc. | Systems and methods for detecting contact between a link and an external object |
EP4171428A1 (en) | 2020-06-30 | 2023-05-03 | Auris Health, Inc. | Robotic medical system with collision proximity indicators |
US11357586B2 (en) | 2020-06-30 | 2022-06-14 | Auris Health, Inc. | Systems and methods for saturated robotic movement |
AU2021356662A1 (en) | 2020-10-07 | 2023-06-15 | Canary Medical Switzerland Ag | Providing medical devices with sensing functionality |
CN113384291A (en) * | 2021-06-11 | 2021-09-14 | 北京华医共享医疗科技有限公司 | Medical ultrasonic detection method and system |
CA3226286A1 (en) | 2021-07-08 | 2023-01-12 | Mendaera, Inc. | Real time image guided portable robotic intervention system |
WO2023235224A1 (en) * | 2022-05-31 | 2023-12-07 | Noah Medical Corporation | Systems and methods for robotic endoscope with integrated tool-in-lesion-tomosynthesis |
Family Cites Families (402)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4745908A (en) | 1987-05-08 | 1988-05-24 | Circon Corporation | Inspection instrument fexible shaft having deflection compensation means |
JP2750201B2 (en) | 1990-04-13 | 1998-05-13 | オリンパス光学工業株式会社 | Endoscope insertion state detection device |
US5550953A (en) | 1994-04-20 | 1996-08-27 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | On-line method and apparatus for coordinated mobility and manipulation of mobile robots |
US5603318A (en) | 1992-04-21 | 1997-02-18 | University Of Utah Research Foundation | Apparatus and method for photogrammetric surgical localization |
US5526812A (en) | 1993-06-21 | 1996-06-18 | General Electric Company | Display system for enhancing visualization of body structures during medical procedures |
US6059718A (en) | 1993-10-18 | 2000-05-09 | Olympus Optical Co., Ltd. | Endoscope form detecting apparatus in which coil is fixedly mounted by insulating member so that form is not deformed within endoscope |
ATE320226T1 (en) | 1994-10-07 | 2006-04-15 | Univ St Louis | SURGICAL NAVIGATION ARRANGEMENT INCLUDING REFERENCE AND LOCATION SYSTEMS |
US6690963B2 (en) | 1995-01-24 | 2004-02-10 | Biosense, Inc. | System for determining the location and orientation of an invasive medical instrument |
US6246898B1 (en) | 1995-03-28 | 2001-06-12 | Sonometrics Corporation | Method for carrying out a medical procedure using a three-dimensional tracking and imaging system |
US5935075A (en) | 1995-09-20 | 1999-08-10 | Texas Heart Institute | Detecting thermal discrepancies in vessel walls |
DE69733249T8 (en) | 1996-02-15 | 2006-04-27 | Biosense Webster, Inc., Diamond Bar | DETERMINATION OF THE EXACT POSITION OF ENDOSCOPES |
AU709081B2 (en) | 1996-02-15 | 1999-08-19 | Biosense, Inc. | Medical procedures and apparatus using intrabody probes |
US6063095A (en) | 1996-02-20 | 2000-05-16 | Computer Motion, Inc. | Method and apparatus for performing minimally invasive surgical procedures |
US6047080A (en) | 1996-06-19 | 2000-04-04 | Arch Development Corporation | Method and apparatus for three-dimensional reconstruction of coronary vessels from angiographic images |
US5831614A (en) | 1996-07-01 | 1998-11-03 | Sun Microsystems, Inc. | X-Y viewport scroll using location of display with respect to a point |
WO1998032388A2 (en) | 1997-01-24 | 1998-07-30 | Koninklijke Philips Electronics N.V. | Image display system |
US6246784B1 (en) | 1997-08-19 | 2001-06-12 | The United States Of America As Represented By The Department Of Health And Human Services | Method for segmenting medical images and detecting surface anomalies in anatomical structures |
US6810281B2 (en) | 2000-12-21 | 2004-10-26 | Endovia Medical, Inc. | Medical mapping system |
FR2779339B1 (en) | 1998-06-09 | 2000-10-13 | Integrated Surgical Systems Sa | MATCHING METHOD AND APPARATUS FOR ROBOTIC SURGERY, AND MATCHING DEVICE COMPRISING APPLICATION |
US6425865B1 (en) | 1998-06-12 | 2002-07-30 | The University Of British Columbia | Robotically assisted medical ultrasound |
AU1525400A (en) | 1998-11-18 | 2000-06-05 | Microdexterity Systems, Inc. | Medical manipulator for use with an imaging device |
US6493608B1 (en) | 1999-04-07 | 2002-12-10 | Intuitive Surgical, Inc. | Aspects of a control system of a minimally invasive surgical apparatus |
US6501981B1 (en) | 1999-03-16 | 2002-12-31 | Accuray, Inc. | Apparatus and method for compensating for respiratory and patient motions during treatment |
US6594552B1 (en) | 1999-04-07 | 2003-07-15 | Intuitive Surgical, Inc. | Grip strength with tactile feedback for robotic surgery |
US7386339B2 (en) | 1999-05-18 | 2008-06-10 | Mediguide Ltd. | Medical imaging and navigation system |
US9572519B2 (en) | 1999-05-18 | 2017-02-21 | Mediguide Ltd. | Method and apparatus for invasive device tracking using organ timing signal generated from MPS sensors |
JP3668865B2 (en) | 1999-06-21 | 2005-07-06 | 株式会社日立製作所 | Surgical device |
US9272416B2 (en) | 1999-09-17 | 2016-03-01 | Intuitive Surgical Operations, Inc. | Phantom degrees of freedom for manipulating the movement of mechanical bodies |
US6466198B1 (en) | 1999-11-05 | 2002-10-15 | Innoventions, Inc. | View navigation and magnification of a hand-held device with a display |
FI114282B (en) | 1999-11-05 | 2004-09-30 | Polar Electro Oy | Method, Arrangement and Heart Rate Monitor for Heartbeat Detection |
US6755797B1 (en) | 1999-11-29 | 2004-06-29 | Bowles Fluidics Corporation | Method and apparatus for producing oscillation of a bladder |
US7747312B2 (en) | 2000-01-04 | 2010-06-29 | George Mason Intellectual Properties, Inc. | System and method for automatic shape registration and instrument tracking |
DE10011790B4 (en) | 2000-03-13 | 2005-07-14 | Siemens Ag | Medical instrument for insertion into an examination subject, and medical examination or treatment device |
US7181289B2 (en) | 2000-03-20 | 2007-02-20 | Pflueger D Russell | Epidural nerve root access catheter and treatment methods |
DE10025285A1 (en) | 2000-05-22 | 2001-12-06 | Siemens Ag | Fully automatic, robot-assisted camera guidance using position sensors for laparoscopic interventions |
DE10033723C1 (en) | 2000-07-12 | 2002-02-21 | Siemens Ag | Surgical instrument position and orientation visualization device for surgical operation has data representing instrument position and orientation projected onto surface of patient's body |
US6865498B2 (en) | 2001-11-30 | 2005-03-08 | Thermwood Corporation | System for calibrating the axes on a computer numeric controlled machining system and method thereof |
US6812842B2 (en) | 2001-12-20 | 2004-11-02 | Calypso Medical Technologies, Inc. | System for excitation of a leadless miniature marker |
AU2003218010A1 (en) | 2002-03-06 | 2003-09-22 | Z-Kat, Inc. | System and method for using a haptic device in combination with a computer-assisted surgery system |
DE10210646A1 (en) | 2002-03-11 | 2003-10-09 | Siemens Ag | Method for displaying a medical instrument brought into an examination area of a patient |
US20050256398A1 (en) | 2004-05-12 | 2005-11-17 | Hastings Roger N | Systems and methods for interventional medicine |
US7998062B2 (en) | 2004-03-29 | 2011-08-16 | Superdimension, Ltd. | Endoscope structures and techniques for navigating to a target in branched structure |
EP3189781A1 (en) | 2002-04-17 | 2017-07-12 | Covidien LP | Endoscope structures and techniques for navigating to a target in branched structure |
US7822466B2 (en) | 2002-04-25 | 2010-10-26 | The Johns Hopkins University | Robot for computed tomography interventions |
JP2005528157A (en) | 2002-06-04 | 2005-09-22 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Hybrid 3D reconstruction of coronary artery structure based on rotational angiography |
JP4439393B2 (en) | 2002-06-17 | 2010-03-24 | メイザー サージカル テクノロジーズ リミテッド | Robots for use with orthopedic inserts |
US20040047044A1 (en) | 2002-06-25 | 2004-03-11 | Dalton Michael Nicholas | Apparatus and method for combining three-dimensional spaces |
KR100449765B1 (en) | 2002-10-12 | 2004-09-22 | 삼성에스디아이 주식회사 | Lithium metal anode for lithium battery |
US6899672B2 (en) | 2002-11-08 | 2005-05-31 | Scimed Life Systems, Inc. | Endoscopic imaging system including removable deflection device |
AU2003278465A1 (en) | 2002-11-13 | 2004-06-03 | Koninklijke Philips Electronics N.V. | Medical viewing system and method for detecting boundary structures |
US7697972B2 (en) | 2002-11-19 | 2010-04-13 | Medtronic Navigation, Inc. | Navigation system for cardiac therapies |
US7599730B2 (en) | 2002-11-19 | 2009-10-06 | Medtronic Navigation, Inc. | Navigation system for cardiac therapies |
US20040186349A1 (en) | 2002-12-24 | 2004-09-23 | Usgi Medical Corp. | Apparatus and methods for achieving endoluminal access |
FR2852226B1 (en) | 2003-03-10 | 2005-07-15 | Univ Grenoble 1 | LOCALIZED MEDICAL INSTRUMENT WITH ORIENTABLE SCREEN |
US7203277B2 (en) | 2003-04-25 | 2007-04-10 | Brainlab Ag | Visualization device and method for combined patient and object image data |
US7822461B2 (en) | 2003-07-11 | 2010-10-26 | Siemens Medical Solutions Usa, Inc. | System and method for endoscopic path planning |
EP2316328B1 (en) | 2003-09-15 | 2012-05-09 | Super Dimension Ltd. | Wrap-around holding device for use with bronchoscopes |
US7835778B2 (en) | 2003-10-16 | 2010-11-16 | Medtronic Navigation, Inc. | Method and apparatus for surgical navigation of a multiple piece construct for implantation |
WO2005058137A2 (en) | 2003-12-12 | 2005-06-30 | University Of Washington | Catheterscope 3d guidance and interface system |
JP2005192632A (en) | 2003-12-26 | 2005-07-21 | Olympus Corp | Subject interior moving state detecting system |
US8021301B2 (en) | 2003-12-26 | 2011-09-20 | Fujifilm Corporation | Ultrasonic image processing apparatus, ultrasonic image processing method and ultrasonic image processing program |
US20050193451A1 (en) | 2003-12-30 | 2005-09-01 | Liposonix, Inc. | Articulating arm for medical procedures |
EP1715788B1 (en) | 2004-02-17 | 2011-09-07 | Philips Electronics LTD | Method and apparatus for registration, verification, and referencing of internal organs |
EP2384715B1 (en) | 2004-03-05 | 2015-07-08 | Hansen Medical, Inc. | Robotic catheter system |
US7850642B2 (en) | 2004-03-05 | 2010-12-14 | Hansen Medical, Inc. | Methods using a robotic catheter system |
US7811294B2 (en) | 2004-03-08 | 2010-10-12 | Mediguide Ltd. | Automatic guidewire maneuvering system and method |
EP4026486A1 (en) | 2004-03-23 | 2022-07-13 | Boston Scientific Medical Device Limited | In-vivo visualization system |
EP1731093B1 (en) | 2004-03-29 | 2013-01-09 | Olympus Corporation | System for detecting position in examinee |
US7720521B2 (en) | 2004-04-21 | 2010-05-18 | Acclarent, Inc. | Methods and devices for performing procedures within the ear, nose, throat and paranasal sinuses |
US7462175B2 (en) | 2004-04-21 | 2008-12-09 | Acclarent, Inc. | Devices, systems and methods for treating disorders of the ear, nose and throat |
US20070208252A1 (en) | 2004-04-21 | 2007-09-06 | Acclarent, Inc. | Systems and methods for performing image guided procedures within the ear, nose, throat and paranasal sinuses |
US8155403B2 (en) | 2004-05-05 | 2012-04-10 | University Of Iowa Research Foundation | Methods and devices for airway tree labeling and/or matching |
US7632265B2 (en) | 2004-05-28 | 2009-12-15 | St. Jude Medical, Atrial Fibrillation Division, Inc. | Radio frequency ablation servo catheter and method |
US20060209019A1 (en) | 2004-06-01 | 2006-09-21 | Energid Technologies Corporation | Magnetic haptic feedback systems and methods for virtual reality environments |
US7772541B2 (en) | 2004-07-16 | 2010-08-10 | Luna Innnovations Incorporated | Fiber optic position and/or shape sensing based on rayleigh scatter |
US20060025668A1 (en) | 2004-08-02 | 2006-02-02 | Peterson Thomas H | Operating table with embedded tracking technology |
US8239002B2 (en) | 2004-08-12 | 2012-08-07 | Novatek Medical Ltd. | Guiding a tool for medical treatment by detecting a source of radioactivity |
US7536216B2 (en) | 2004-10-18 | 2009-05-19 | Siemens Medical Solutions Usa, Inc. | Method and system for virtual endoscopy with guidance for biopsy |
US9049954B2 (en) | 2004-11-03 | 2015-06-09 | Cambridge International, Inc. | Hanger bar assembly for architectural mesh and the like |
CA2587857C (en) | 2004-11-23 | 2017-10-10 | Pneumrx, Inc. | Steerable device for accessing a target site and methods |
US8611983B2 (en) | 2005-01-18 | 2013-12-17 | Philips Electronics Ltd | Method and apparatus for guiding an instrument to a target in the lung |
US8335357B2 (en) | 2005-03-04 | 2012-12-18 | Kabushiki Kaisha Toshiba | Image processing apparatus |
US20060258935A1 (en) | 2005-05-12 | 2006-11-16 | John Pile-Spellman | System for autonomous robotic navigation |
US10555775B2 (en) | 2005-05-16 | 2020-02-11 | Intuitive Surgical Operations, Inc. | Methods and system for performing 3-D tool tracking by fusion of sensor and/or camera derived data during minimally invasive robotic surgery |
US7756563B2 (en) | 2005-05-23 | 2010-07-13 | The Penn State Research Foundation | Guidance method based on 3D-2D pose estimation and 3D-CT registration with application to live bronchoscopy |
US7889905B2 (en) * | 2005-05-23 | 2011-02-15 | The Penn State Research Foundation | Fast 3D-2D image registration method with application to continuously guided endoscopy |
JP4813190B2 (en) | 2005-05-26 | 2011-11-09 | オリンパスメディカルシステムズ株式会社 | Capsule medical device |
GB2428110A (en) | 2005-07-06 | 2007-01-17 | Armstrong Healthcare Ltd | A robot and method of registering a robot. |
JP2009501563A (en) | 2005-07-14 | 2009-01-22 | エンハンスド・メデイカルシステム・エルエルシー | Robot for minimizing invasive procedures |
US8583220B2 (en) | 2005-08-02 | 2013-11-12 | Biosense Webster, Inc. | Standardization of catheter-based treatment for atrial fibrillation |
US8657814B2 (en) | 2005-08-22 | 2014-02-25 | Medtronic Ablation Frontiers Llc | User interface for tissue ablation system |
US9661991B2 (en) | 2005-08-24 | 2017-05-30 | Koninklijke Philips N.V. | System, method and devices for navigated flexible endoscopy |
US20070066881A1 (en) | 2005-09-13 | 2007-03-22 | Edwards Jerome R | Apparatus and method for image guided accuracy verification |
US20070073136A1 (en) | 2005-09-15 | 2007-03-29 | Robert Metzger | Bone milling with image guided surgery |
EP3788944B1 (en) | 2005-11-22 | 2024-02-28 | Intuitive Surgical Operations, Inc. | System for determining the shape of a bendable instrument |
US8303505B2 (en) * | 2005-12-02 | 2012-11-06 | Abbott Cardiovascular Systems Inc. | Methods and apparatuses for image guided medical procedures |
US8190238B2 (en) | 2005-12-09 | 2012-05-29 | Hansen Medical, Inc. | Robotic catheter system and methods |
DE102005059271B4 (en) | 2005-12-12 | 2019-02-21 | Siemens Healthcare Gmbh | catheter device |
US8672922B2 (en) | 2005-12-20 | 2014-03-18 | Intuitive Surgical Operations, Inc. | Wireless communication in a robotic surgical system |
US9266239B2 (en) | 2005-12-27 | 2016-02-23 | Intuitive Surgical Operations, Inc. | Constraint based control in a minimally invasive surgical apparatus |
US7930065B2 (en) | 2005-12-30 | 2011-04-19 | Intuitive Surgical Operations, Inc. | Robotic surgery system including position sensors using fiber bragg gratings |
US9962066B2 (en) | 2005-12-30 | 2018-05-08 | Intuitive Surgical Operations, Inc. | Methods and apparatus to shape flexible entry guides for minimally invasive surgery |
US9186046B2 (en) | 2007-08-14 | 2015-11-17 | Koninklijke Philips Electronics N.V. | Robotic instrument systems and methods utilizing optical fiber sensor |
US8191359B2 (en) | 2006-04-13 | 2012-06-05 | The Regents Of The University Of California | Motion estimation using hidden markov model processing in MRI and other applications |
WO2007129616A1 (en) | 2006-05-02 | 2007-11-15 | National University Corporation Nagoya University | Insertion assist system of endoscope and insertion assist method of endoscope |
DE102006021373A1 (en) | 2006-05-08 | 2007-11-15 | Siemens Ag | X-ray diagnostic device |
WO2007141784A2 (en) | 2006-06-05 | 2007-12-13 | Technion Research & Development Foundation Ltd. | Controlled steering of a flexible needle |
KR101477738B1 (en) | 2006-06-13 | 2014-12-31 | 인튜어티브 서지컬 인코포레이티드 | Minimally invasive surgical system |
US7505810B2 (en) | 2006-06-13 | 2009-03-17 | Rhythmia Medical, Inc. | Non-contact cardiac mapping, including preprocessing |
US8040127B2 (en) | 2006-08-15 | 2011-10-18 | General Electric Company | Multi-sensor distortion mapping method and system |
US8150498B2 (en) | 2006-09-08 | 2012-04-03 | Medtronic, Inc. | System for identification of anatomical landmarks |
US7824328B2 (en) | 2006-09-18 | 2010-11-02 | Stryker Corporation | Method and apparatus for tracking a surgical instrument during surgery |
CN100546540C (en) | 2006-09-19 | 2009-10-07 | 上海宏桐实业有限公司 | Endocardium three-dimension navigation system |
US7940977B2 (en) * | 2006-10-25 | 2011-05-10 | Rcadia Medical Imaging Ltd. | Method and system for automatic analysis of blood vessel structures to identify calcium or soft plaque pathologies |
WO2008125910A2 (en) | 2006-11-10 | 2008-10-23 | Superdimension, Ltd. | Adaptive navigation technique for navigating a catheter through a body channel or cavity |
US7936922B2 (en) | 2006-11-22 | 2011-05-03 | Adobe Systems Incorporated | Method and apparatus for segmenting images |
BRPI0718950A2 (en) | 2006-12-01 | 2013-12-17 | Thomson Licensing | ESTIMATION OF AN OBJECT LOCATION IN AN IMAGE |
US9220439B2 (en) | 2006-12-29 | 2015-12-29 | St. Jude Medical, Atrial Fibrillation Division, Inc. | Navigational reference dislodgement detection method and system |
US20080183188A1 (en) | 2007-01-25 | 2008-07-31 | Warsaw Orthopedic, Inc. | Integrated Surgical Navigational and Neuromonitoring System |
US20080183068A1 (en) | 2007-01-25 | 2008-07-31 | Warsaw Orthopedic, Inc. | Integrated Visualization of Surgical Navigational and Neural Monitoring Information |
US20080183064A1 (en) | 2007-01-30 | 2008-07-31 | General Electric Company | Multi-sensor distortion detection method and system |
US8672836B2 (en) | 2007-01-31 | 2014-03-18 | The Penn State Research Foundation | Method and apparatus for continuous guidance of endoscopy |
US9037215B2 (en) | 2007-01-31 | 2015-05-19 | The Penn State Research Foundation | Methods and apparatus for 3D route planning through hollow organs |
US8146874B2 (en) | 2007-02-02 | 2012-04-03 | Hansen Medical, Inc. | Mounting support assembly for suspending a medical instrument driver above an operating table |
JP4914735B2 (en) | 2007-02-14 | 2012-04-11 | オリンパスメディカルシステムズ株式会社 | Endoscope system for controlling the position of the treatment tool |
EP2143038A4 (en) | 2007-02-20 | 2011-01-26 | Philip L Gildenberg | Videotactic and audiotactic assisted surgical methods and procedures |
WO2010058398A2 (en) | 2007-03-08 | 2010-05-27 | Sync-Rx, Ltd. | Image processing and tool actuation for medical procedures |
US8821376B2 (en) | 2007-03-12 | 2014-09-02 | David Tolkowsky | Devices and methods for performing medical procedures in tree-like luminal structures |
WO2008135985A1 (en) * | 2007-05-02 | 2008-11-13 | Earlysense Ltd | Monitoring, predicting and treating clinical episodes |
US8934961B2 (en) | 2007-05-18 | 2015-01-13 | Biomet Manufacturing, Llc | Trackable diagnostic scope apparatus and methods of use |
US20080300478A1 (en) | 2007-05-30 | 2008-12-04 | General Electric Company | System and method for displaying real-time state of imaged anatomy during a surgical procedure |
US20090030307A1 (en) | 2007-06-04 | 2009-01-29 | Assaf Govari | Intracorporeal location system with movement compensation |
US9089256B2 (en) | 2008-06-27 | 2015-07-28 | Intuitive Surgical Operations, Inc. | Medical robotic system providing an auxiliary view including range of motion limitations for articulatable instruments extending out of a distal end of an entry guide |
US9084623B2 (en) | 2009-08-15 | 2015-07-21 | Intuitive Surgical Operations, Inc. | Controller assisted reconfiguration of an articulated instrument during movement into and out of an entry guide |
US9138129B2 (en) | 2007-06-13 | 2015-09-22 | Intuitive Surgical Operations, Inc. | Method and system for moving a plurality of articulated instruments in tandem back towards an entry guide |
US20080319491A1 (en) | 2007-06-19 | 2008-12-25 | Ryan Schoenefeld | Patient-matched surgical component and methods of use |
US20130165945A9 (en) | 2007-08-14 | 2013-06-27 | Hansen Medical, Inc. | Methods and devices for controlling a shapeable instrument |
US20090076476A1 (en) | 2007-08-15 | 2009-03-19 | Hansen Medical, Inc. | Systems and methods employing force sensing for mapping intra-body tissue |
WO2009097461A1 (en) | 2008-01-29 | 2009-08-06 | Neoguide Systems Inc. | Apparatus and methods for automatically controlling an endoscope |
EP2633811B1 (en) | 2008-02-12 | 2015-09-16 | Covidien LP | Controlled perspective guidance method |
KR100927096B1 (en) | 2008-02-27 | 2009-11-13 | 아주대학교산학협력단 | Method for object localization using visual images with reference coordinates |
US20090228020A1 (en) | 2008-03-06 | 2009-09-10 | Hansen Medical, Inc. | In-situ graft fenestration |
US8219179B2 (en) | 2008-03-06 | 2012-07-10 | Vida Diagnostics, Inc. | Systems and methods for navigation within a branched structure of a body |
US8808164B2 (en) | 2008-03-28 | 2014-08-19 | Intuitive Surgical Operations, Inc. | Controlling a robotic surgical tool with a display monitor |
US9002076B2 (en) | 2008-04-15 | 2015-04-07 | Medtronic, Inc. | Method and apparatus for optimal trajectory planning |
US8532734B2 (en) | 2008-04-18 | 2013-09-10 | Regents Of The University Of Minnesota | Method and apparatus for mapping a structure |
US8218846B2 (en) | 2008-05-15 | 2012-07-10 | Superdimension, Ltd. | Automatic pathway and waypoint generation and navigation method |
JP5372407B2 (en) | 2008-05-23 | 2013-12-18 | オリンパスメディカルシステムズ株式会社 | Medical equipment |
US20100030061A1 (en) | 2008-07-31 | 2010-02-04 | Canfield Monte R | Navigation system for cardiac therapies using gating |
US8290571B2 (en) | 2008-08-01 | 2012-10-16 | Koninklijke Philips Electronics N.V. | Auxiliary cavity localization |
ES2608820T3 (en) | 2008-08-15 | 2017-04-17 | Stryker European Holdings I, Llc | System and method of visualization of the inside of a body |
US8848974B2 (en) | 2008-09-29 | 2014-09-30 | Restoration Robotics, Inc. | Object-tracking systems and methods |
US8781630B2 (en) | 2008-10-14 | 2014-07-15 | University Of Florida Research Foundation, Inc. | Imaging platform to provide integrated navigation capabilities for surgical guidance |
KR101642164B1 (en) | 2008-10-31 | 2016-07-22 | 셰브론 필립스 케미컬 컴퍼니 엘피 | Compositions and catalyst systems of metal precursors and olefinic diluents |
US20100121139A1 (en) | 2008-11-12 | 2010-05-13 | Ouyang Xiaolong | Minimally Invasive Imaging Systems |
US20100125284A1 (en) | 2008-11-20 | 2010-05-20 | Hansen Medical, Inc. | Registered instrument movement integration |
US8457714B2 (en) * | 2008-11-25 | 2013-06-04 | Magnetecs, Inc. | System and method for a catheter impedance seeking device |
WO2010068783A1 (en) | 2008-12-12 | 2010-06-17 | Corindus Inc. | Remote catheter procedure system |
US8335590B2 (en) | 2008-12-23 | 2012-12-18 | Intuitive Surgical Operations, Inc. | System and method for adjusting an image capturing device attribute using an unused degree-of-freedom of a master control device |
JP4585048B2 (en) | 2009-01-15 | 2010-11-24 | オリンパスメディカルシステムズ株式会社 | Endoscope system |
KR100961661B1 (en) | 2009-02-12 | 2010-06-09 | 주식회사 래보 | Apparatus and method of operating a medical navigation system |
US8120301B2 (en) | 2009-03-09 | 2012-02-21 | Intuitive Surgical Operations, Inc. | Ergonomic surgeon control console in robotic surgical systems |
US8337397B2 (en) | 2009-03-26 | 2012-12-25 | Intuitive Surgical Operations, Inc. | Method and system for providing visual guidance to an operator for steering a tip of an endoscopic device toward one or more landmarks in a patient |
US9002427B2 (en) | 2009-03-30 | 2015-04-07 | Lifewave Biomedical, Inc. | Apparatus and method for continuous noninvasive measurement of respiratory function and events |
RU2529380C2 (en) | 2009-04-29 | 2014-09-27 | Конинклейке Филипс Электроникс Н.В. | Estimation of depth in real time by monocular endoscope images |
JP2012525898A (en) | 2009-05-08 | 2012-10-25 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Real-time scope tracking and branch labeling without electromagnetic tracking and preoperative roadmap scanning |
US8675736B2 (en) | 2009-05-14 | 2014-03-18 | Qualcomm Incorporated | Motion vector processing |
CN102292991B (en) | 2009-05-15 | 2014-10-08 | 夏普株式会社 | Image processing device and image processing method |
BRPI1007726A2 (en) | 2009-05-18 | 2017-01-31 | Koninl Philips Electronics Nv | Image-to-image registration method, Image-to-image registration system, Guided endoscopy camera position calibration method and Guided endoscopy camera calibration system |
US20100292565A1 (en) | 2009-05-18 | 2010-11-18 | Andreas Meyer | Medical imaging medical device navigation from at least two 2d projections from different angles |
ES2388029B1 (en) | 2009-05-22 | 2013-08-13 | Universitat Politècnica De Catalunya | ROBOTIC SYSTEM FOR LAPAROSCOPIC SURGERY. |
US8718338B2 (en) * | 2009-07-23 | 2014-05-06 | General Electric Company | System and method to compensate for respiratory motion in acquired radiography images |
GB0915200D0 (en) | 2009-09-01 | 2009-10-07 | Ucl Business Plc | Method for re-localising sites in images |
US20110092808A1 (en) | 2009-10-20 | 2011-04-21 | Magnetecs, Inc. | Method for acquiring high density mapping data with a catheter guidance system |
EP2496128A1 (en) | 2009-11-04 | 2012-09-12 | Koninklijke Philips Electronics N.V. | Collision avoidance and detection using distance sensors |
JP4781492B2 (en) | 2009-11-10 | 2011-09-28 | オリンパスメディカルシステムズ株式会社 | Articulated manipulator device and endoscope system having the same |
WO2011094518A2 (en) | 2010-01-28 | 2011-08-04 | The Penn State Research Foundation | Image-based global registration system and method applicable to bronchoscopy guidance |
EP2377457B1 (en) | 2010-02-22 | 2016-07-27 | Olympus Corporation | Medical apparatus |
DE102010012621A1 (en) | 2010-03-24 | 2011-09-29 | Siemens Aktiengesellschaft | Method and device for automatically adapting a reference image |
US8425455B2 (en) | 2010-03-30 | 2013-04-23 | Angiodynamics, Inc. | Bronchial catheter and method of use |
IT1401669B1 (en) | 2010-04-07 | 2013-08-02 | Sofar Spa | ROBOTIC SURGERY SYSTEM WITH PERFECT CONTROL. |
US8581905B2 (en) | 2010-04-08 | 2013-11-12 | Disney Enterprises, Inc. | Interactive three dimensional displays on handheld devices |
WO2011134083A1 (en) | 2010-04-28 | 2011-11-03 | Ryerson University | System and methods for intraoperative guidance feedback |
US8845631B2 (en) | 2010-04-28 | 2014-09-30 | Medtronic Ablation Frontiers Llc | Systems and methods of performing medical procedures |
US20120101369A1 (en) | 2010-06-13 | 2012-04-26 | Angiometrix Corporation | Methods and systems for determining vascular bodily lumen information and guiding medical devices |
US8672837B2 (en) | 2010-06-24 | 2014-03-18 | Hansen Medical, Inc. | Methods and devices for controlling a shapeable medical device |
US8460236B2 (en) | 2010-06-24 | 2013-06-11 | Hansen Medical, Inc. | Fiber optic instrument sensing system |
US20130303887A1 (en) | 2010-08-20 | 2013-11-14 | Veran Medical Technologies, Inc. | Apparatus and method for four dimensional soft tissue navigation |
US20120191107A1 (en) | 2010-09-17 | 2012-07-26 | Tanner Neal A | Systems and methods for positioning an elongate member inside a body |
JP5669529B2 (en) | 2010-11-17 | 2015-02-12 | オリンパス株式会社 | Imaging apparatus, program, and focus control method |
DE112010006052T5 (en) | 2010-12-08 | 2013-10-10 | Industrial Technology Research Institute | Method for generating stereoscopic views of monoscopic endoscopic images and systems using them |
US8812079B2 (en) | 2010-12-22 | 2014-08-19 | Biosense Webster (Israel), Ltd. | Compensation for magnetic disturbance due to fluoroscope |
US9414770B2 (en) | 2010-12-29 | 2016-08-16 | Biosense Webster (Israel) Ltd. | Respiratory effect reduction in catheter position sensing |
US20120191086A1 (en) | 2011-01-20 | 2012-07-26 | Hansen Medical, Inc. | System and method for endoluminal and translumenal therapy |
KR101964579B1 (en) | 2011-02-18 | 2019-04-03 | 디퍼이 신테스 프로덕츠, 인코포레이티드 | Tool with integrated navigation and guidance system and related apparatus and methods |
US10391277B2 (en) | 2011-02-18 | 2019-08-27 | Voxel Rad, Ltd. | Systems and methods for 3D stereoscopic angiovision, angionavigation and angiotherapeutics |
US10362963B2 (en) | 2011-04-14 | 2019-07-30 | St. Jude Medical, Atrial Fibrillation Division, Inc. | Correction of shift and drift in impedance-based medical device navigation using magnetic field information |
US10918307B2 (en) | 2011-09-13 | 2021-02-16 | St. Jude Medical, Atrial Fibrillation Division, Inc. | Catheter navigation using impedance and magnetic field measurements |
US8900131B2 (en) | 2011-05-13 | 2014-12-02 | Intuitive Surgical Operations, Inc. | Medical system providing dynamic registration of a model of an anatomical structure for image-guided surgery |
US9572481B2 (en) | 2011-05-13 | 2017-02-21 | Intuitive Surgical Operations, Inc. | Medical system with multiple operating modes for steering a medical instrument through linked body passages |
US9675304B2 (en) | 2011-06-27 | 2017-06-13 | Koninklijke Philips N.V. | Live 3D angiogram using registration of a surgical tool curve to an X-ray image |
US9173683B2 (en) | 2011-08-31 | 2015-11-03 | DePuy Synthes Products, Inc. | Revisable orthopedic anchor and methods of use |
CN102973317A (en) | 2011-09-05 | 2013-03-20 | 周宁新 | Arrangement structure for mechanical arm of minimally invasive surgery robot |
US8849388B2 (en) | 2011-09-08 | 2014-09-30 | Apn Health, Llc | R-wave detection method |
EP2755591B1 (en) | 2011-09-16 | 2020-11-18 | Auris Health, Inc. | System for displaying an image of a patient anatomy on a movable display |
US9504604B2 (en) | 2011-12-16 | 2016-11-29 | Auris Surgical Robotics, Inc. | Lithotripsy eye treatment |
US8700561B2 (en) | 2011-12-27 | 2014-04-15 | Mcafee, Inc. | System and method for providing data protection workflows in a network environment |
US9636040B2 (en) | 2012-02-03 | 2017-05-02 | Intuitive Surgical Operations, Inc. | Steerable flexible needle with embedded shape sensing |
JP6261516B2 (en) | 2012-02-09 | 2018-01-17 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | Shaft tracker for real-time navigation tracking |
EP4056111A3 (en) | 2012-02-22 | 2022-12-07 | Veran Medical Technologies, Inc. | Systems, methods, and devices for four dimensional soft tissue navigation |
US10383765B2 (en) | 2012-04-24 | 2019-08-20 | Auris Health, Inc. | Apparatus and method for a global coordinate system for use in robotic surgery |
US20140142591A1 (en) | 2012-04-24 | 2014-05-22 | Auris Surgical Robotics, Inc. | Method, apparatus and a system for robotic assisted surgery |
US10039473B2 (en) | 2012-05-14 | 2018-08-07 | Intuitive Surgical Operations, Inc. | Systems and methods for navigation based on ordered sensor records |
JP2015528713A (en) | 2012-06-21 | 2015-10-01 | グローバス メディカル インコーポレイティッド | Surgical robot platform |
US10194801B2 (en) | 2012-06-28 | 2019-02-05 | Koninklijke Philips N.V. | Fiber optic sensor guided navigation for vascular visualization and monitoring |
DE102012220116A1 (en) | 2012-06-29 | 2014-01-02 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Mobile device, in particular for processing or observation of a body, and method for handling, in particular calibration, of a device |
US9183354B2 (en) | 2012-08-15 | 2015-11-10 | Musc Foundation For Research Development | Systems and methods for image guided surgery |
CA2893369A1 (en) | 2012-08-24 | 2014-02-27 | University Of Houston | Robotic device and systems for image-guided and robot-assisted surgery |
US20140180063A1 (en) | 2012-10-12 | 2014-06-26 | Intuitive Surgical Operations, Inc. | Determining position of medical device in branched anatomical structure |
US20140107390A1 (en) | 2012-10-12 | 2014-04-17 | Elekta Ab (Publ) | Implementation and experimental results of real-time 4d tumor tracking using multi-leaf collimator (mlc), and/or mlc-carriage (mlc-bank), and/or treatment table (couch) |
US9121455B2 (en) | 2012-11-07 | 2015-09-01 | Dana Automotive Systems Group, Llc | Clutch management system |
WO2014081725A2 (en) | 2012-11-20 | 2014-05-30 | University Of Washington Through Its Center For Commercialization | Electromagnetic sensor integration with ultrathin scanning fiber endoscope |
LU92104B1 (en) | 2012-11-28 | 2014-05-30 | Iee Sarl | Method and system for determining a ventilatory threshold |
JP6045417B2 (en) | 2012-12-20 | 2016-12-14 | オリンパス株式会社 | Image processing apparatus, electronic apparatus, endoscope apparatus, program, and operation method of image processing apparatus |
US10231867B2 (en) | 2013-01-18 | 2019-03-19 | Auris Health, Inc. | Method, apparatus and system for a water jet |
US11172809B2 (en) | 2013-02-15 | 2021-11-16 | Intuitive Surgical Operations, Inc. | Vision probe with access port |
WO2014133476A1 (en) | 2013-02-26 | 2014-09-04 | Kabakci Ahmet Sinan | A robotic manipulator system |
US9459087B2 (en) | 2013-03-05 | 2016-10-04 | Ezono Ag | Magnetic position detection system |
JP5715311B2 (en) | 2013-03-06 | 2015-05-07 | オリンパスメディカルシステムズ株式会社 | Endoscope system |
US10149720B2 (en) | 2013-03-08 | 2018-12-11 | Auris Health, Inc. | Method, apparatus, and a system for facilitating bending of an instrument in a surgical or medical robotic environment |
US9867635B2 (en) | 2013-03-08 | 2018-01-16 | Auris Surgical Robotics, Inc. | Method, apparatus and system for a water jet |
US10080576B2 (en) | 2013-03-08 | 2018-09-25 | Auris Health, Inc. | Method, apparatus, and a system for facilitating bending of an instrument in a surgical or medical robotic environment |
US20140296655A1 (en) | 2013-03-11 | 2014-10-02 | ROPAMedics LLC | Real-time tracking of cerebral hemodynamic response (rtchr) of a subject based on hemodynamic parameters |
CN104780826B (en) | 2013-03-12 | 2016-12-28 | 奥林巴斯株式会社 | Endoscopic system |
US9057600B2 (en) | 2013-03-13 | 2015-06-16 | Hansen Medical, Inc. | Reducing incremental measurement sensor error |
US20170303941A1 (en) | 2013-03-14 | 2017-10-26 | The General Hospital Corporation | System and method for guided removal from an in vivo subject |
US9014851B2 (en) | 2013-03-15 | 2015-04-21 | Hansen Medical, Inc. | Systems and methods for tracking robotically controlled medical instruments |
US9271663B2 (en) | 2013-03-15 | 2016-03-01 | Hansen Medical, Inc. | Flexible instrument localization from both remote and elongation sensors |
US9301723B2 (en) | 2013-03-15 | 2016-04-05 | Covidien Lp | Microwave energy-delivery device and system |
US20170238807A9 (en) | 2013-03-15 | 2017-08-24 | LX Medical, Inc. | Tissue imaging and image guidance in luminal anatomic structures and body cavities |
US10271810B2 (en) | 2013-04-02 | 2019-04-30 | St. Jude Medical International Holding S.à r. l. | Enhanced compensation of motion in a moving organ using processed reference sensor data |
US20140309527A1 (en) | 2013-04-12 | 2014-10-16 | Ninepoint Medical, Inc. | Multiple aperture, multiple modal optical systems and methods |
US9592095B2 (en) | 2013-05-16 | 2017-03-14 | Intuitive Surgical Operations, Inc. | Systems and methods for robotic medical system integration with external imaging |
US11020016B2 (en) | 2013-05-30 | 2021-06-01 | Auris Health, Inc. | System and method for displaying anatomy and devices on a movable display |
US20140364739A1 (en) | 2013-06-06 | 2014-12-11 | General Electric Company | Systems and methods for analyzing a vascular structure |
US10744035B2 (en) | 2013-06-11 | 2020-08-18 | Auris Health, Inc. | Methods for robotic assisted cataract surgery |
JP6037964B2 (en) | 2013-07-26 | 2016-12-07 | オリンパス株式会社 | Manipulator system |
US10426661B2 (en) | 2013-08-13 | 2019-10-01 | Auris Health, Inc. | Method and apparatus for laser assisted cataract surgery |
JP6785656B2 (en) | 2013-08-15 | 2020-11-18 | インテュイティブ サージカル オペレーションズ, インコーポレイテッド | Graphical user interface for catheter positioning and insertion |
US10098565B2 (en) | 2013-09-06 | 2018-10-16 | Covidien Lp | System and method for lung visualization using ultrasound |
CN105592790A (en) | 2013-10-02 | 2016-05-18 | 皇家飞利浦有限公司 | Hub design and methods for optical shape sensing registration |
US9737373B2 (en) | 2013-10-24 | 2017-08-22 | Auris Surgical Robotics, Inc. | Instrument device manipulator and surgical drape |
JP6656148B2 (en) | 2013-10-24 | 2020-03-04 | オーリス ヘルス インコーポレイテッド | System and associated method for robot-assisted endoluminal surgery |
US9314191B2 (en) | 2013-11-19 | 2016-04-19 | Pacesetter, Inc. | Method and system to measure cardiac motion using a cardiovascular navigation system |
EP3079625B1 (en) | 2013-12-09 | 2023-09-13 | Intuitive Surgical Operations, Inc. | Systems and non-surgical methods for device-aware flexible tool registration |
CN103705307B (en) | 2013-12-10 | 2017-02-22 | 中国科学院深圳先进技术研究院 | Surgical navigation system and medical robot |
CN103735313B (en) | 2013-12-11 | 2016-08-17 | 中国科学院深圳先进技术研究院 | A kind of operating robot and state monitoring method thereof |
EP3085324B1 (en) | 2013-12-20 | 2019-02-20 | Olympus Corporation | Guide member for flexible manipulator, and flexible manipulator |
EP3084747B1 (en) | 2013-12-20 | 2022-12-14 | Intuitive Surgical Operations, Inc. | Simulator system for medical procedure training |
US11617623B2 (en) * | 2014-01-24 | 2023-04-04 | Koninklijke Philips N.V. | Virtual image with optical shape sensing device perspective |
CN106170265B (en) | 2014-02-04 | 2020-06-30 | 直观外科手术操作公司 | System and method for non-rigid deformation of tissue for virtual navigation of interventional tools |
SG11201606423VA (en) | 2014-02-05 | 2016-09-29 | Univ Singapore | Systems and methods for tracking and displaying endoscope shape and distal end orientation |
US20150223902A1 (en) | 2014-02-07 | 2015-08-13 | Hansen Medical, Inc. | Navigation with 3d localization using 2d images |
WO2015121311A1 (en) | 2014-02-11 | 2015-08-20 | KB Medical SA | Sterile handle for controlling a robotic surgical system from a sterile field |
JP6270537B2 (en) | 2014-02-27 | 2018-01-31 | オリンパス株式会社 | Medical system |
KR20150103938A (en) | 2014-03-04 | 2015-09-14 | 현대자동차주식회사 | A separation membrane for lithium sulfur batteries |
US10952751B2 (en) | 2014-03-17 | 2021-03-23 | Marksman Targeting, Inc. | Surgical targeting systems and methods |
CN104931059B (en) | 2014-03-21 | 2018-09-11 | 比亚迪股份有限公司 | Vehicle-mounted rescue navigation system and method |
US10912523B2 (en) * | 2014-03-24 | 2021-02-09 | Intuitive Surgical Operations, Inc. | Systems and methods for anatomic motion compensation |
US10046140B2 (en) | 2014-04-21 | 2018-08-14 | Hansen Medical, Inc. | Devices, systems, and methods for controlling active drive systems |
US20150305650A1 (en) | 2014-04-23 | 2015-10-29 | Mark Hunter | Apparatuses and methods for endobronchial navigation to and confirmation of the location of a target tissue and percutaneous interception of the target tissue |
CN104055520B (en) | 2014-06-11 | 2016-02-24 | 清华大学 | Human organ motion monitoring method and operation guiding system |
US10792464B2 (en) | 2014-07-01 | 2020-10-06 | Auris Health, Inc. | Tool and method for using surgical endoscope with spiral lumens |
US20170007337A1 (en) | 2014-07-01 | 2017-01-12 | Auris Surgical Robotics, Inc. | Driver-mounted torque sensing mechanism |
US20160270865A1 (en) | 2014-07-01 | 2016-09-22 | Auris Surgical Robotics, Inc. | Reusable catheter with disposable balloon attachment and tapered tip |
US10159533B2 (en) | 2014-07-01 | 2018-12-25 | Auris Health, Inc. | Surgical system with configurable rail-mounted mechanical arms |
US9744335B2 (en) | 2014-07-01 | 2017-08-29 | Auris Surgical Robotics, Inc. | Apparatuses and methods for monitoring tendons of steerable catheters |
US9788910B2 (en) | 2014-07-01 | 2017-10-17 | Auris Surgical Robotics, Inc. | Instrument-mounted tension sensing mechanism for robotically-driven medical instruments |
US9561083B2 (en) | 2014-07-01 | 2017-02-07 | Auris Surgical Robotics, Inc. | Articulating flexible endoscopic tool with roll capabilities |
AU2015284085B2 (en) | 2014-07-02 | 2019-07-18 | Covidien Lp | System and method of providing distance and orientation feedback while navigating in 3D |
US20160000414A1 (en) | 2014-07-02 | 2016-01-07 | Covidien Lp | Methods for marking biopsy location |
US9603668B2 (en) * | 2014-07-02 | 2017-03-28 | Covidien Lp | Dynamic 3D lung map view for tool navigation inside the lung |
US9770216B2 (en) | 2014-07-02 | 2017-09-26 | Covidien Lp | System and method for navigating within the lung |
CN107427327A (en) * | 2014-09-30 | 2017-12-01 | 奥瑞斯外科手术机器人公司 | Configurable robotic surgical system with virtual track and soft endoscope |
CA2964459A1 (en) | 2014-10-15 | 2016-04-21 | Vincent Suzara | Magnetic field structures, field generators, navigation and imaging for untethered robotic device enabled medical procedure |
US10314463B2 (en) * | 2014-10-24 | 2019-06-11 | Auris Health, Inc. | Automated endoscope calibration |
DE102014222293A1 (en) | 2014-10-31 | 2016-05-19 | Siemens Aktiengesellschaft | Method for automatically monitoring the penetration behavior of a trocar held by a robot arm and monitoring system |
EP3217977A1 (en) | 2014-11-11 | 2017-09-20 | Vanderbilt University | Methods for limiting acute kidney injury |
KR102425170B1 (en) | 2014-11-13 | 2022-07-26 | 인튜어티브 서지컬 오퍼레이션즈 인코포레이티드 | Systems and methods for filtering localization data |
JP6342794B2 (en) | 2014-12-25 | 2018-06-13 | 新光電気工業株式会社 | Wiring board and method of manufacturing wiring board |
US9931168B2 (en) | 2015-01-12 | 2018-04-03 | Biomet Manufacuturing. LLC | Plan implementation |
WO2016134297A1 (en) | 2015-02-20 | 2016-08-25 | Nostix, Llc | Medical device position location systems, devices and methods |
JP6348078B2 (en) | 2015-03-06 | 2018-06-27 | 富士フイルム株式会社 | Branch structure determination apparatus, operation method of branch structure determination apparatus, and branch structure determination program |
JP6371729B2 (en) | 2015-03-25 | 2018-08-08 | 富士フイルム株式会社 | Endoscopy support apparatus, operation method of endoscopy support apparatus, and endoscope support program |
US20160287279A1 (en) | 2015-04-01 | 2016-10-06 | Auris Surgical Robotics, Inc. | Microsurgical tool for robotic applications |
WO2016164824A1 (en) | 2015-04-09 | 2016-10-13 | Auris Surgical Robotics, Inc. | Surgical system with configurable rail-mounted mechanical arms |
CN104758066B (en) | 2015-05-06 | 2017-05-10 | 中国科学院深圳先进技术研究院 | Equipment for surgical navigation and surgical robot |
WO2016187054A1 (en) | 2015-05-15 | 2016-11-24 | Auris Surgical Robotics, Inc. | Surgical robotics system |
WO2016199273A1 (en) * | 2015-06-11 | 2016-12-15 | オリンパス株式会社 | Endoscope device and operation method for endoscope device |
US10347904B2 (en) | 2015-06-19 | 2019-07-09 | Solidenergy Systems, Llc | Multi-layer polymer coated Li anode for high density Li metal battery |
GB2540757B (en) | 2015-07-22 | 2021-03-31 | Cmr Surgical Ltd | Torque sensors |
WO2017030913A2 (en) * | 2015-08-14 | 2017-02-23 | Intuitive Surgical Operations, Inc. | Systems and methods of registration for image-guided surgery |
WO2017030915A1 (en) * | 2015-08-14 | 2017-02-23 | Intuitive Surgical Operations, Inc. | Systems and methods of registration for image-guided surgery |
CN107920861B (en) | 2015-08-28 | 2021-08-17 | 皇家飞利浦有限公司 | Device for determining a kinematic relationship |
CN113367671A (en) | 2015-08-31 | 2021-09-10 | 梅西莫股份有限公司 | Wireless patient monitoring system and method |
KR102429651B1 (en) | 2015-09-09 | 2022-08-05 | 아우리스 헬스, 인크. | Instrument Device Manipulator for Surgical Robot System |
CN114098599A (en) | 2015-09-17 | 2022-03-01 | 恩达马斯特有限公司 | Endoscope system |
US9727963B2 (en) | 2015-09-18 | 2017-08-08 | Auris Surgical Robotics, Inc. | Navigation of tubular networks |
WO2017066108A1 (en) | 2015-10-13 | 2017-04-20 | Lightlab Imaging, Inc. | Intravascular imaging system and methods to determine cut plane view angle of side branch |
US9955986B2 (en) | 2015-10-30 | 2018-05-01 | Auris Surgical Robotics, Inc. | Basket apparatus |
US9949749B2 (en) | 2015-10-30 | 2018-04-24 | Auris Surgical Robotics, Inc. | Object capture with a basket |
US10231793B2 (en) | 2015-10-30 | 2019-03-19 | Auris Health, Inc. | Object removal through a percutaneous suction tube |
JP6218991B2 (en) | 2015-11-13 | 2017-10-25 | オリンパス株式会社 | Method of operating endoscope state estimation apparatus and endoscope system |
US10143526B2 (en) | 2015-11-30 | 2018-12-04 | Auris Health, Inc. | Robot-assisted driving systems and methods |
US11172895B2 (en) | 2015-12-07 | 2021-11-16 | Covidien Lp | Visualization, navigation, and planning with electromagnetic navigation bronchoscopy and cone beam computed tomography integrated |
CN105511881A (en) | 2015-12-10 | 2016-04-20 | 中国航空工业集团公司西安航空计算技术研究所 | General airborne interactive data management method |
CN105559850B (en) | 2015-12-17 | 2017-08-25 | 天津工业大学 | It is a kind of to be used for the surgical drill apparatus that robot assisted surgery has power sensing function |
US10932861B2 (en) | 2016-01-14 | 2021-03-02 | Auris Health, Inc. | Electromagnetic tracking surgical system and method of controlling the same |
US10973422B2 (en) | 2016-01-22 | 2021-04-13 | Fitbit, Inc. | Photoplethysmography-based pulse wave analysis using a wearable device |
US10932691B2 (en) | 2016-01-26 | 2021-03-02 | Auris Health, Inc. | Surgical tools having electromagnetic tracking components |
US10470719B2 (en) | 2016-02-01 | 2019-11-12 | Verily Life Sciences Llc | Machine learnt model to detect REM sleep periods using a spectral analysis of heart rate and motion |
US10717194B2 (en) | 2016-02-26 | 2020-07-21 | Intuitive Surgical Operations, Inc. | System and method for collision avoidance using virtual boundaries |
US10729917B2 (en) | 2016-03-24 | 2020-08-04 | Koninklijke Philips N.V. | Treatment assessment device |
US20190105112A1 (en) | 2016-03-31 | 2019-04-11 | Koninklijke Philips N.V. | Image guided robot for catheter placement |
US11324554B2 (en) | 2016-04-08 | 2022-05-10 | Auris Health, Inc. | Floating electromagnetic field generator system and method of controlling the same |
US10470839B2 (en) | 2016-06-02 | 2019-11-12 | Covidien Lp | Assessment of suture or staple line integrity and localization of potential tissue defects along the suture or staple line |
US10806516B2 (en) | 2016-06-20 | 2020-10-20 | General Electric Company | Virtual 4D stent implantation path assessment |
KR102420386B1 (en) | 2016-06-30 | 2022-07-13 | 인튜어티브 서지컬 오퍼레이션즈 인코포레이티드 | Graphical user interface for displaying guidance information in multiple modes during video-guided procedures |
US11037464B2 (en) | 2016-07-21 | 2021-06-15 | Auris Health, Inc. | System with emulator movement tracking for controlling medical devices |
KR102555546B1 (en) | 2016-08-31 | 2023-07-19 | 아우리스 헬스, 인코포레이티드 | length-preserving surgical instruments |
US10238455B2 (en) | 2016-08-31 | 2019-03-26 | Covidien Lp | Pathway planning for use with a navigation planning and procedure system |
US20180055576A1 (en) | 2016-09-01 | 2018-03-01 | Covidien Lp | Respiration motion stabilization for lung magnetic navigation system |
US9931025B1 (en) | 2016-09-30 | 2018-04-03 | Auris Surgical Robotics, Inc. | Automated calibration of endoscopes with pull wires |
US10278778B2 (en) | 2016-10-27 | 2019-05-07 | Inneroptic Technology, Inc. | Medical device navigation using a virtual 3D space |
US10136959B2 (en) | 2016-12-28 | 2018-11-27 | Auris Health, Inc. | Endolumenal object sizing |
US10244926B2 (en) | 2016-12-28 | 2019-04-02 | Auris Health, Inc. | Detecting endolumenal buckling of flexible instruments |
US10543048B2 (en) | 2016-12-28 | 2020-01-28 | Auris Health, Inc. | Flexible instrument insertion using an adaptive insertion force threshold |
US11842030B2 (en) | 2017-01-31 | 2023-12-12 | Medtronic Navigation, Inc. | Method and apparatus for image-based navigation |
AU2018244318B2 (en) | 2017-03-28 | 2023-11-16 | Auris Health, Inc. | Shaft actuating handle |
US10475235B2 (en) | 2017-03-29 | 2019-11-12 | Fujifilm Corporation | Three-dimensional image processing apparatus, three-dimensional image processing method, and three-dimensional image processing program |
WO2018183727A1 (en) | 2017-03-31 | 2018-10-04 | Auris Health, Inc. | Robotic systems for navigation of luminal networks that compensate for physiological noise |
KR20230106716A (en) | 2017-04-07 | 2023-07-13 | 아우리스 헬스, 인코포레이티드 | Patient introducer alignment |
US10285574B2 (en) | 2017-04-07 | 2019-05-14 | Auris Health, Inc. | Superelastic medical instrument |
US20180308247A1 (en) | 2017-04-25 | 2018-10-25 | Best Medical International, Inc. | Tissue imaging system and method for tissue imaging |
CN110831498B (en) | 2017-05-12 | 2022-08-12 | 奥瑞斯健康公司 | Biopsy device and system |
JP7301750B2 (en) | 2017-05-17 | 2023-07-03 | オーリス ヘルス インコーポレイテッド | Interchangeable working channel |
US10022192B1 (en) | 2017-06-23 | 2018-07-17 | Auris Health, Inc. | Automatically-initialized robotic systems for navigation of luminal networks |
JP7317723B2 (en) | 2017-06-28 | 2023-07-31 | オーリス ヘルス インコーポレイテッド | Electromagnetic field distortion detection |
WO2019005872A1 (en) | 2017-06-28 | 2019-01-03 | Auris Health, Inc. | Instrument insertion compensation |
US11026758B2 (en) | 2017-06-28 | 2021-06-08 | Auris Health, Inc. | Medical robotics systems implementing axis constraints during actuation of one or more motorized joints |
JP7330902B2 (en) | 2017-06-28 | 2023-08-22 | オーリス ヘルス インコーポレイテッド | Electromagnetic distortion detection |
US10426559B2 (en) | 2017-06-30 | 2019-10-01 | Auris Health, Inc. | Systems and methods for medical instrument compression compensation |
US10593052B2 (en) * | 2017-08-23 | 2020-03-17 | Synaptive Medical (Barbados) Inc. | Methods and systems for updating an existing landmark registration |
US10464209B2 (en) | 2017-10-05 | 2019-11-05 | Auris Health, Inc. | Robotic system with indication of boundary for robotic arm |
US10145747B1 (en) | 2017-10-10 | 2018-12-04 | Auris Health, Inc. | Detection of undesirable forces on a surgical robotic arm |
US10016900B1 (en) | 2017-10-10 | 2018-07-10 | Auris Health, Inc. | Surgical robotic arm admittance control |
US10555778B2 (en) | 2017-10-13 | 2020-02-11 | Auris Health, Inc. | Image-based branch detection and mapping for navigation |
US11058493B2 (en) | 2017-10-13 | 2021-07-13 | Auris Health, Inc. | Robotic system configured for navigation path tracing |
KR102645922B1 (en) | 2017-12-06 | 2024-03-13 | 아우리스 헬스, 인코포레이티드 | Systems and methods for correcting non-directed instrument rolls |
WO2019113389A1 (en) | 2017-12-08 | 2019-06-13 | Auris Health, Inc. | Directed fluidics |
JP7314136B2 (en) | 2017-12-08 | 2023-07-25 | オーリス ヘルス インコーポレイテッド | Systems and methods for navigation and targeting of medical instruments |
CN111770736A (en) | 2017-12-11 | 2020-10-13 | 奥瑞斯健康公司 | System and method for instrument-based insertion architecture |
US11510736B2 (en) | 2017-12-14 | 2022-11-29 | Auris Health, Inc. | System and method for estimating instrument location |
CN110809453B (en) | 2017-12-18 | 2023-06-06 | 奥瑞斯健康公司 | Method and system for instrument tracking and navigation within a luminal network |
KR102264368B1 (en) | 2018-01-17 | 2021-06-17 | 아우리스 헬스, 인코포레이티드 | Surgical platform with adjustable arm support |
JP7463277B2 (en) | 2018-01-17 | 2024-04-08 | オーリス ヘルス インコーポレイテッド | Surgical robotic system having improved robotic arm |
EP3752085A4 (en) | 2018-02-13 | 2021-11-24 | Auris Health, Inc. | System and method for driving medical instrument |
KR20200136931A (en) | 2018-03-01 | 2020-12-08 | 아우리스 헬스, 인코포레이티드 | Methods and systems for mapping and navigation |
JP2019154816A (en) * | 2018-03-13 | 2019-09-19 | ソニー・オリンパスメディカルソリューションズ株式会社 | Medical image processor, medical observation device and operation method of medical observation device |
EP3773304A4 (en) | 2018-03-28 | 2021-12-22 | Auris Health, Inc. | Systems and methods for displaying estimated location of instrument |
US11109920B2 (en) | 2018-03-28 | 2021-09-07 | Auris Health, Inc. | Medical instruments with variable bending stiffness profiles |
CN110891469B (en) | 2018-03-28 | 2023-01-13 | 奥瑞斯健康公司 | System and method for registration of positioning sensors |
WO2019191265A1 (en) | 2018-03-29 | 2019-10-03 | Auris Health, Inc. | Robotically-enabled medical systems with multifunction end effectors having rotational offsets |
US10905499B2 (en) | 2018-05-30 | 2021-02-02 | Auris Health, Inc. | Systems and methods for location sensor-based branch prediction |
MX2020012904A (en) | 2018-05-31 | 2021-02-26 | Auris Health Inc | Image-based airway analysis and mapping. |
KR102567087B1 (en) | 2018-05-31 | 2023-08-17 | 아우리스 헬스, 인코포레이티드 | Robotic systems and methods for navigation of luminal networks detecting physiological noise |
CN110831481B (en) | 2018-05-31 | 2022-08-30 | 奥瑞斯健康公司 | Path-based navigation of tubular networks |
US10744981B2 (en) | 2018-06-06 | 2020-08-18 | Sensata Technologies, Inc. | Electromechanical braking connector |
MX2020013241A (en) | 2018-06-07 | 2021-02-22 | Auris Health Inc | Robotic medical systems with high force instruments. |
WO2020005348A1 (en) | 2018-06-27 | 2020-01-02 | Auris Health, Inc. | Alignment and attachment systems for medical instruments |
WO2020005370A1 (en) | 2018-06-27 | 2020-01-02 | Auris Health, Inc. | Systems and techniques for providing multiple perspectives during medical procedures |
US11399905B2 (en) | 2018-06-28 | 2022-08-02 | Auris Health, Inc. | Medical systems incorporating pulley sharing |
CN112804946A (en) | 2018-08-07 | 2021-05-14 | 奥瑞斯健康公司 | Combining strain-based shape sensing with catheter control |
US10828118B2 (en) | 2018-08-15 | 2020-11-10 | Auris Health, Inc. | Medical instruments for tissue cauterization |
CN112566567A (en) | 2018-08-17 | 2021-03-26 | 奥瑞斯健康公司 | Bipolar medical instrument |
WO2020041619A2 (en) | 2018-08-24 | 2020-02-27 | Auris Health, Inc. | Manually and robotically controllable medical instruments |
CN112739283A (en) | 2018-09-17 | 2021-04-30 | 奥瑞斯健康公司 | System and method for accompanying medical procedure |
WO2020068303A1 (en) | 2018-09-26 | 2020-04-02 | Auris Health, Inc. | Systems and instruments for suction and irrigation |
CN112804933A (en) | 2018-09-26 | 2021-05-14 | 奥瑞斯健康公司 | Articulating medical device |
EP3856001A4 (en) | 2018-09-28 | 2022-06-22 | Auris Health, Inc. | Devices, systems, and methods for manually and robotically driving medical instruments |
KR20210073542A (en) | 2018-09-28 | 2021-06-18 | 아우리스 헬스, 인코포레이티드 | Systems and methods for docking medical instruments |
WO2020069404A1 (en) | 2018-09-28 | 2020-04-02 | Auris Health, Inc. | Robotic systems and methods for concomitant endoscopic and percutaneous medical procedures |
US11576738B2 (en) | 2018-10-08 | 2023-02-14 | Auris Health, Inc. | Systems and instruments for tissue sealing |
WO2020131529A1 (en) | 2018-12-20 | 2020-06-25 | Auris Health, Inc. | Shielding for wristed instruments |
JP2022515835A (en) | 2018-12-28 | 2022-02-22 | オーリス ヘルス インコーポレイテッド | Percutaneous sheath for robotic medical systems and methods |
EP3890645A4 (en) | 2019-02-22 | 2022-09-07 | Auris Health, Inc. | Surgical platform with motorized arms for adjustable arm supports |
US10945904B2 (en) | 2019-03-08 | 2021-03-16 | Auris Health, Inc. | Tilt mechanisms for medical systems and applications |
US20200297444A1 (en) | 2019-03-21 | 2020-09-24 | The Board Of Trustees Of The Leland Stanford Junior University | Systems and methods for localization based on machine learning |
CN113613580A (en) | 2019-03-22 | 2021-11-05 | 奥瑞斯健康公司 | System and method for aligning inputs on a medical instrument |
WO2020197625A1 (en) | 2019-03-25 | 2020-10-01 | Auris Health, Inc. | Systems and methods for medical stapling |
US11617627B2 (en) | 2019-03-29 | 2023-04-04 | Auris Health, Inc. | Systems and methods for optical strain sensing in medical instruments |
EP3952779A4 (en) | 2019-04-08 | 2023-01-18 | Auris Health, Inc. | Systems, methods, and workflows for concomitant procedures |
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