Classifications

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  • A61F2/02—Prostheses implantable into the body
  • A61F2/24—Heart valves ; Vascular valves, e.g. venous valves; Heart implants, e.g. passive devices for improving the function of the native valve or the heart muscle; Transmyocardial revascularisation [TMR] devices; Valves implantable in the body
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  • A61F2/2412—Heart valves ; Vascular valves, e.g. venous valves; Heart implants, e.g. passive devices for improving the function of the native valve or the heart muscle; Transmyocardial revascularisation [TMR] devices; Valves implantable in the body with soft flexible valve members, e.g. tissue valves shaped like natural valves
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  • A61F2/02—Prostheses implantable into the body
  • A61F2/24—Heart valves ; Vascular valves, e.g. venous valves; Heart implants, e.g. passive devices for improving the function of the native valve or the heart muscle; Transmyocardial revascularisation [TMR] devices; Valves implantable in the body
  • A61F2/2412—Heart valves ; Vascular valves, e.g. venous valves; Heart implants, e.g. passive devices for improving the function of the native valve or the heart muscle; Transmyocardial revascularisation [TMR] devices; Valves implantable in the body with soft flexible valve members, e.g. tissue valves shaped like natural valves
  • A61F2/2415—Manufacturing methods
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• • A—HUMAN NECESSITIES
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  • A61F2240/00—Manufacturing or designing of prostheses classified in groups A61F2/00 - A61F2/26 or A61F2/82 or A61F9/00 or A61F11/00 or subgroups thereof
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• • A—HUMAN NECESSITIES
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  • A61L—METHODS OR APPARATUS FOR STERILISING MATERIALS OR OBJECTS IN GENERAL; DISINFECTION, STERILISATION OR DEODORISATION OF AIR; CHEMICAL ASPECTS OF BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES; MATERIALS FOR BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES
  • A61L2430/00—Materials or treatment for tissue regeneration
  • A61L2430/20—Materials or treatment for tissue regeneration for reconstruction of the heart, e.g. heart valves
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  • G06V2201/031—Recognition of patterns in medical or anatomical images of internal organs

Definitions

• SAW transcatheter aortic valve replacement
• TAVR transcatheter aortic valve replacement
• pre-TAVR evaluations may involve accurately reconstructing the patient’s anatomy, generating a computational mesh, and using it for Finite Element Analysis (FEA) or/and Computational Fluid Dynamics (CFD) simulations.
• FFA Finite Element Analysis
• CFD Computational Fluid Dynamics
• An exemplary system and method are disclosed that can be used to (1) generate a parametric heart valve model (e.g., aortic valve model or mitral valve model) with a few manually selected landmarks; (2) rapid automatic reconstruct the aorta and aortic valve leaflet geometries of a patient (e.g., for clinical pre-TAVR evaluation or other pre-procedural planning evaluation); (3) output readily usable computational mesh (e.g., for pre-TAVR in-silico studies, such as evaluation for coronary obstruction, root rapture and predicting valve hemodynamics).
• the exemplary system and method may be used for patient-specific computational or 3D printing evaluations that require the input of the patient geometry.
• the exemplary system and method is configured to provide an automatic method to reconstruct the patient-specific geometries and generate meshes for in-silico studies with just a few manual inputs (about 5-10 minutes per case).
• a user selects several landmark points related to the aortic valve.
• a parametric leaflet model is used to represent the valve geometry, followed by an automatic aortic root reconstruction algorithm that can extract the shape of the aorta.
• the coronary arteries and calcium deposits are obtained automatically using a region-growing algorithm.
• the exemplary system and method generates a mesh for computational studies by combining all the mesh components. A visual inspection and possible manual clean-up are performed to ensure the accuracy and quality of the mesh.
• a heart valve model includes a parametric leaflet model representing geometries of a heart valve constructed based on at least one landmark point associated with the heart valve.
• the heart valve model includes a ray casted anatomical structure constructed based on an automatic aortic root reconstruction algorithm to extract a shape of an aorta.
• the heart valve also includes a ray casted anatomical structure constructed based on an automatic aortic root reconstruction algorithm to extract a shape of an aorta.
• a heart valve model includes multi-point ray casting of an aorta.
• an anatomical structure model includes a first anatomical structure including a parametric leaflet model representing the first anatomical structure constructed based on at least one landmark point associated with the first anatomical structure.
• the anatomical structure model includes a second anatomical structure including a ray casted second anatomical structure constructed based on an automatic anatomical structure reconstruction algorithm to extract geometries of the second anatomical structure.
• the anatomical structure model also includes a third anatomical structure. The first, second, and third anatomical structures are combined to generate the anatomical structure model.
• Figure l is a flow chart of a semi-automatic patient-specific mesh generation process.
• Figures 2A-2E are illustrations of an auto segmentation process.
• Figure 2A shows the locations of the thirteen landmark points.
• Figure 2B shows the parametric leaflet model with landmarks.
• Figure 2C shows aortic root with parametric leaflets.
• Figure 2D shows coronary arteries and calcium deposits.
• Figure 2E shows the final mesh output.
• Figures 3A-3C are illustrations of the parametric leaflet generation process.
• Figure 3A shows a skeleton connecting the landmarks using second order polynomials.
• Figure 3B shows multiple second-order polynomials fitted to represent the leaflet surface.
• Figure 3C shows the final triangular mesh.
• Figures 4A-4F show samples of the parametric leaflets.
• Figures 4A-4C show three individual leaflets and Figures 4D-4F show full aortic valve.
• Figure 5 shows sample comparisons between the parametric leaflets (light gray) and the original image slices.
• Figures 6A-6D are illustrations of an aorta reconstruction process.
• Figure 6A shows detected aortic wall using a single-point ray-casting algorithm.
• Figure 6B shows detected aortic wall using a multi-point ray-casting algorithm, wherein the edge detection accuracy has improved dramatically compared to that in Figure 6A.
• Figure 6C shows samples of the aortic wall detection results.
• Figure 6D shows the final aorta model and mesh.
• Figure 7 shows comparisons between automatic (left) and manual (right) segmentation results.
• Figure 8 shows an illustrative computer architecture for a computer system 200 capable of executing the software components that can use the output of the exemplary method described herein.
• the computer architecture shown in Figure 8 illustrates an example computer system configuration, and the computer 200 can be utilized to execute any aspects of the components and/or modules presented herein described as executing on the analysis system or any components in communication therewith.
• a flow chart of a semi-automatic mesh generation process is shown in Figure 1.
• the algorithm consists of three main functions: (1) A parametric leaflet model, (2) a method to accurately determine the aortic root/aorta geometry, and (3) a method to assemble different components and output a final mesh for computational simulations.
• a user picks several landmark points related to the aortic valve.
• the exemplary system and method employ a parametric leaflet model to represent the valve geometry, which is then processed by an automatic aortic root reconstruction algorithm that can extract the shape of the aorta. Then, the
• exemplary system and method is configured to determine the coronary arteries and calcium deposits automatically using a region growing algorithm.
• the exemplary system and method generate a mesh for computational studies by combining all the mesh components. A visual inspection and surface manual clean-up are performed to ensure the accuracy and quality of the mesh.
• Figure 2 shows a detailed illustration of an example auto segmentation process employed in the method of Figure 1.
• a total of 13 different landmarks are chosen for the parametric leaflet model.
• the points are: Po at the center coaptation point of the three leaflets; Pi-3 and P4-6 are six commissural points. These two sets of points are aimed at resolving the finite coaptation height between the leaflets at the commissure.
• Pi and Pi are at the lower and upper ends of this finite commissural coaptation line ( Figure 2B).
• P1-9 are three surface points on the leaflets, while P10-12 are the three leaflet hinges.
• the landmarks can be hand-picked by a clinician or can be obtained by artificial intelligence (A.I.) and/or machining learning approaches. The number and location of the landmarks are not limited to the described ones.
• Figure 2B shows the distributions of the landmark points in the parametric aortic valve model. With the guidance of these landmarks and the parametric leaflet model, an aortic root geometry
• 5 is constructed in a slice-by-slice way using a multi-point ray-casting method described later.
• Figure 2C shows the reconstructed aortic root geometry with the parametric leaflets attached to it. Unlike many other parametric models, to ensure the output mesh quality in this invention, the leaflets are stitched to the aortic root to form a water-tight connection. Two extra landmarks at the coronary ostia are needed for auto segmenting the coronary arteries by an intensity-based region-growing method. Similarly, the calcium inside the aortic root domain is obtained by the same intensity -based segmentation method.
• Figure 2D shows the coronary artery and calcium geometries.
• the last step is to assemble the mesh to construct a computational mesh used in FEA/CFD simulations. Since the parametric leaflets and the aortic root are all surface meshes, specific thickness values are assigned to them based on physiological data. The three leaflets are separated by a finite distance comparable to the leaflet thickness to ensure their opening during numerical simulations. The final mesh is shown in Figure 2E.
• a parametric leaflet model maybe constructed based on selected landmarks (e.g., based on the thirteen landmark points in the example described above). There may be seven points on an individual leaflet surface (one center point, four commissural points, one point on the surface, and one hinge point).
• Figure 3 shows an example parametric leaflet generation process of the method of Figure 1
• a leaflet skeleton is generated, in some aspects, using second-order polynomials connecting these points (Figure 3A). Then, multiple second-order polynomials are fitted to the skeleton to create a surface (Figure 3B). Finally, triangular meshes are used to represent the leaflet ( Figure 3C). Samples of the leaflet model are shown in Figure 4. The way to generate the leaflet surface is not limited
• NURBS non-uniform Rational B-splines
• Figure 4 shows example parametric leaflets models that can be generated in the method of Figure 1.
• Figures 4A-4C show three individual leaflet meshes, and the corresponding aortic valve model is shown in Figures 4D-4E.
• This parametric leaflet model can handle complex surface geometries and capture an individual leaflet’s irregular shape, as demonstrated in Figures 4 A-4C.
• the extraction of the aortic root/aorta geometry may be based on an intensity-based, slice-by-slice, multi-point ray-casting edge detection algorithm (Figure 6).
• Figure 6 shows an example aorta reconstruction process of the method of Figure 1.
• the aortic annulus may be readily defined by the three leaflet hinge points. Slices parallel to the aortic annulus are extracted from the original 3D CT data. Starting with one point in the blood domain and casting rays in all directions, one can extract the intensity variations along the rays. A sudden jump of the intensity value from high (blood) to low (tissue) indicates the aortic wall ( Figure 6A). The contour of the aortic root in this slice is generated by connecting all the detected edge points. However, errors may occur during this edge detection process due to noise or poor image quality ( Figure 6A). To address this issue, a modified method that casts rays from a series of origins is introduced. Since most rays from different origins should
• this algorithm demonstrates its ability to capture the aortic walls accurately from the left ventricular outflow graft to the ascending aorta.
• the ray origins are different for all the slices ( Figure 3C).
• a five-point core and four satellite points derived from the neighboring slice's boundaries are used to improve the accuracy of edge detection.
• the contours from all the slices are stacked on top of each other to represent the wall of the aorta ( Figure 6D).
• the aorta geometry is smoothed by fitting a sixth-order polynomial to the points along the same longitude in the assembling process. The fitting method is not limited to polynomials. Other methods such as B-spline fit can be used as well.
• Triangle meshes are used to construct this geometry ( Figure 6D).
• Other surface mesh types such as rectangle or pentagon meshes can also be used.
• Both coronary arteries and calcium deposit geometries are segmented by an intensity- based region-growing algorithm.
• the coordinates of the two coronary ostia need to be specified (human input) as the starting points for the region growing algorithm.
• a global threshold of 850 Hounsfield Units (HU) is applied as suggested in, followed by a cleaning process that discards calcium blobs smaller than 20 voxels or located outside the aorta.
• the final mesh assembling process is based on Boolean operations in the intensity space.
• the surface meshes of the aorta and the leaflets are thickened based on physiological values and voxelized. Later the coronary arteries and calcium deposits are added to the domain using Boolean operations. To prevent the leaflets from fusing, slits with the same width as the leaflet thickness are placed between them. Finally, a marching cubes algorithm converts the voxel data into an STL mesh. The assembled mesh will be visually inspected and corrected for mesh problems if there are any.
• Figure 7 shows a comparison between an example output of an automatic segmentation operation and a manual segmentation operation in the method of Figure 1.
• the exemplary system and method may be in daily clinical practices for TAVR and other structural heart presurgery evaluations.
• the exemplary system and method may be incorporated into
• Parametric heart valve models have been used in segmentation to address the issue of the time-consuming segmentation process in cases where the leaflets are hardly visible in medical images.
• Ionasec, R. et al. used parametric aorta and leaflet models to reconstruct both aortic and mitral valves from 4D cardiac CT and TEE.
• a learning-based algorithm was applied to the 4D images to identify and track landmarks throughout a cardiac cycle.
• a medial representation method was implemented by Pouch A.M. et al. to model the mitral valves from 3D echo images. They managed to capture the thickness of the mitral valve and its deformation during a cardiac cycle. The method has also been applied to reconstruct the anatomy of the aortic valve from 3D echo data (Pouch 2015).
• Figure 8 shows an illustrative computer architecture for a computer system 200 capable of executing the software components that can use the output of the exemplary method described herein.
• the computer architecture shown in Figure 8 illustrates an example computer system configuration, and the computer 200 can be utilized to execute any aspects of the components and/or modules presented herein described as executing on the analysis system or any components in communication therewith.
• the computing device 200 may comprise two or more computers in communication with each other that collaborate to perform a task.
• an application may be partitioned in such a way as to permit concurrent and/or parallel processing of the instructions of the application.
• the data processed by the application may be partitioned in such a way as to permit concurrent and/or parallel processing of different portions of a data set by the two or more computers.
• virtualization software may be employed by the computing device 200 to provide the functionality of a number of servers that is not directly bound to the number of computers in the computing device 200.
• virtualization software may provide twenty virtual servers on four physical computers.
• the functionality disclosed above may be provided by executing the application and/or applications in a cloud computing environment.
• Cloud computing may comprise providing computing services via a network connection
• Cloud computing may be supported, at least in part, by virtualization software.
• a cloud computing environment may be established by an enterprise and/or may be hired on an as-needed basis from a third-party provider.
• Some cloud computing environments may comprise cloud computing resources owned and operated by the enterprise as well as cloud computing resources hired and/or leased from a third-party provider.
• computing device 200 typically includes at least one processing unit 220 and system memory 230.
• system memory 230 may be volatile (such as random-access memory (RAM)), nonvolatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two.
• the processing unit 220 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the computing device 200. While only one processing unit 220 is shown, multiple processors may be present. As used herein, processing unit and processor refers to a physical hardware device that executes encoded instructions for performing functions on inputs and creating outputs, including, for example, but not limited to, microprocessors (MCUs), microcontrollers, graphical processing units (GPUs), and application specific circuits (ASICs). Thus, while instructions may be discussed as executed by a processor, the instructions may be executed simultaneously, serially, or otherwise executed by one or multiple processors.
• the computing device 200 may also include a bus or other communication mechanism for communicating information among various components of the computing device 200.
• Computing device 200 may have additional features/functionality.
• computing device 200 may include additional storage such as removable storage 240 and non-removable storage 250 including, but not limited to, magnetic or optical disks or tapes.
• additional storage such as removable storage 240 and non-removable storage 250 including, but not limited to, magnetic or optical disks or tapes.
• Computing device 200 may include additional storage such as removable storage 240 and non-removable storage 250 including, but not limited to, magnetic or optical disks or tapes.
• the network connection(s) 280 may take the form of modems, modem banks, Ethernet cards, universal serial bus (USB) interface cards, serial interfaces, token ring cards, fiber distributed data interface (FDDI) cards, wireless local area network (WLAN) cards, radio transceiver cards such as code division multiple access (CDMA), global system for mobile communications (GSM), long-term evolution (LTE), worldwide interoperability for microwave access (WiMAX), and/or other air interface protocol radio transceiver cards, and other well-known network devices.
• CDMA code division multiple access
• GSM global system for mobile communications
• LTE long-term evolution
• WiMAX worldwide interoperability for microwave access
• other air interface protocol radio transceiver cards and other well-known network devices.
• Computing device 200 may also have input device(s) 270 such as keyboards, keypads, switches, dials, mice, track balls, touch screens, voice recognizers, card readers, paper tape readers, or other well-known input devices.
• Output device(s) 260 such as printers, video monitors, liquid crystal displays (LCDs), touch screen displays, displays, speakers, etc. may also be included.
• the additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 200. All these devices are well known in the art and need not be discussed at length here.
• the processing unit 220 may be configured to execute program code encoded in tangible, computer-readable media.
• Tangible, computer-readable media refers to any media that is capable of providing data that causes the computing device 200 (i.e., a machine) to operate in a particular fashion.
• Various computer-readable media may be utilized to provide instructions to the processing unit 220 for execution.
• Example tangible, computer-readable media may include, but is not limited to, volatile media, non-volatile media, removable media and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
• System memory 230, removable storage 240, and non-removable storage 250 are all examples of tangible, computer storage media.
• 13 media include, but are not limited to, an integrated circuit (e.g., field-programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.
• an integrated circuit e.g., field-programmable gate array or application-specific IC
• a hard disk e.g., an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical
• the computer architecture 200 may include other types of computing devices, including hand-held computers, embedded computer systems, personal digital assistants, and other types of computing devices known to those skilled in the art. It is also contemplated that the computer architecture 200 may not include all of the components shown in Figure 8, may include other components that are not explicitly shown in Figure 8, or may utilize an architecture different than that shown in Figure 8.
• the processing unit 220 may execute program code stored in the system memory 230.
• the bus may carry data to the system memory 230, from which the processing unit 220 receives and executes instructions.
• the data received by the system memory 230 may optionally be stored on the removable storage 240 or the non-removable storage 250 before or after execution by the processing unit 220.
• the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.
• One or more programs may implement or utilize the processes described in connection with the presently disclosed subject matter, e.g., through the use of an application programming interface (API), reusable controls, or the like.
• API application programming interface
• Such programs may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system.
• the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language and it may be combined with hardware implementations.
• the various components may be in communication via wireless and/or hardwire or other desirable and available communication means, systems and hardware.
• various components and modules may be substituted with other modules or components that provide similar functions.
• the computer architecture 200 includes software and/or hardware components and modules needed to enable the function of the modeling, simulation, and methods disclosed in the present disclosure.
• the computer architecture 200 may include artificial intelligence (A.I.) modules or algorithms and/or machine learning (M.L.) modules or algorithms (e.g., stored in the system memory 230, removable storage 240, non-removable storage 250, and/or a cloud database).
• A.I. and/or M.L. modules/algorithms may improve the predictive power of the models, simulations, and/or methods disclosed in the present disclosure. For example, by using a deep learning, A.I., and/or M.L.
• the computer architecture 200 may include virtual reality (VR), augmented reality (AR) and/or mixed reality display(s), headset(s), glass(es), or any other suitable display device(s) as a part of the output device(s) 260 and/or the input device(s) 270.
• the display device(s) may be interactive to allow an user to select from options including with or without AR, with or without VR, or fused with real time clinical imaging to help clinician interact and make decisions.
• a "subject” may be any applicable human, animal, or other organism, living or dead, or other biological or molecular structure or chemical environment, and may relate to particular components of the subject, for instance specific tissues or fluids of a subject (e.g., human tissue in a particular area of the body of a living subject), which may be in a particular location of the subject, referred to herein as an "area of interest” or a "region of interest.”
• a subject may be a human or any animal.
• an animal may be a variety of any applicable type, including, but not limited thereto, mammal, veterinarian animal, livestock animal or pet type animal, etc.
• the animal may be a laboratory animal specifically selected to have certain characteristics similar to human (e.g., rat, dog, pig, monkey), etc.
• the subject may be any applicable human patient, for example.
• the exemplary system and method can significantly reduce the manual effort needed to build the patient-specific model from several hours to only a few minutes. It will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the invention. Other aspects of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the methods disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

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• 2022
  • 2022-05-11 CA CA3217999A patent/CA3217999A1/en active Pending
  • 2022-05-11 WO PCT/US2022/072240 patent/WO2022241425A1/en not_active Ceased
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  • 2022-05-11 EP EP22808525.4A patent/EP4337140A4/en active Pending
  • 2022-05-11 JP JP2023570184A patent/JP2024519784A/ja active Pending
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