JP7244280B2 - MEDICAL IMAGE DIAGNOSTIC APPARATUS AND MEDICAL IMAGE DIAGNOSTIC METHOD - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to a medical image diagnostic apparatus and a medical image diagnostic method.

Conventionally, regarding image diagnostic devices such as X-ray CT devices, methods of setting image processing parameters by machine learning, detection objects themselves (for example, coronary arteries) in images and AL (Anatomical Landmark: anatomical features) in detection objects A technique for reducing the processing load in the image analysis stage by performing machine learning on a template image including points) is disclosed.

Further, conventionally, there is a technique called ALD (Adaptive Layer Distribution) that automatically sets a scan range by recognizing characteristic points of a human body (for example, contours of the head and body).

However, with the conventional technique, there are cases where it is not possible to easily adjust the imaging position of the subject at the time of imaging by reflecting the results of machine learning.

U.S. Patent Application Publication No. 2016/209995 U.S. Patent Application Publication No. 2017/347973 U.S. Patent Application Publication No. 2018/70908

A problem to be solved by the present invention is to facilitate positioning of a subject during imaging.

A medical image diagnostic apparatus according to an embodiment includes an acquisition unit and a processing unit. The acquisition unit acquires information about the scan target region and the alignment image of the subject. The processing unit applies the acquired information about the scan target region and the subject to a trained model that outputs the scan range of the subject at the time of scanning based on the information about the scan target region and the alignment image of the subject. By inputting the alignment image, the acquired information on the scan target region and the scan range related to the subject related to the alignment image of the subject are output.

1 is a configuration diagram of an X-ray CT apparatus according to a first embodiment; FIG. 4 is a diagram showing an example of data stored in a memory 41; FIG. 4 is a configuration diagram of a scan control function 55; FIG. FIG. 11 is a diagram for explaining the contents of processing by a scan range automatic setting function 55-2; FIG. 4 is a diagram for explaining processing of a learning function 57; FIG. 2 is a diagram for explaining an example of the usage environment of the X-ray CT apparatus 1; FIG. 2 is a diagram for explaining an example of the usage environment of the X-ray CT apparatus 1; 4 is a flowchart showing an example of the flow of imaging processing by the X-ray CT apparatus 1; 6 is a flowchart showing an example of the flow of learning processing by a learning function 57; The figure for demonstrating 10 A of gantry apparatuses which concern on 2nd Embodiment. The figure which shows an example of the data stored in memory 41A which concerns on 2nd Embodiment. FIG. 7 is a configuration diagram of a scan control function 55A according to the second embodiment; FIG. 10 is a diagram for explaining the contents of processing by a scan range automatic setting function 55-2A; The figure for demonstrating the process of 57 A of learning functions. 4 is a flow chart showing an example of the flow of imaging processing by the X-ray CT apparatus 1A. 5 is a flow chart showing an example of the flow of an optical image learning process by a learning function 57A.

A medical image diagnostic apparatus and a medical image diagnostic method according to embodiments will be described below with reference to the drawings. Medical image diagnostic devices include, for example, X-ray CT (Computed Tomography) devices, MRI (Magnetic Resonance Imaging) devices, PET (Positron Emission Tomography) devices, and other medical images. is a device for diagnosing a subject by performing processing for In the following description, the medical image diagnostic apparatus is described as being an X-ray CT apparatus, but it is not limited to this.

(First embodiment)
FIG. 1 is a configuration diagram of an X-ray CT apparatus 1 according to the first embodiment. The X-ray CT apparatus 1 has a gantry device 10, a bed device 30, and a console device 40, for example. For convenience of explanation, FIG. 1 shows both a view of the gantry device 10 viewed from the Z-axis direction and a view of the gantry device 10 viewed from the X-axis direction. In this embodiment, the rotation axis of the rotating frame 17 in the non-tilt state or the longitudinal direction of the top plate 33 of the bed device 30 is the Z-axis direction, and the axis perpendicular to the Z-axis direction and horizontal to the floor surface is X A direction orthogonal to the axial direction and the Z-axis direction and perpendicular to the floor surface is defined as the Y-axis direction.

The gantry device 10 includes, for example, an X-ray tube 11, a wedge 12, a collimator 13, an X-ray high voltage device 14, an X-ray detector 15, and a data acquisition system (DAS: Data Acquisition System) 16. , a rotating frame 17 and a control device 18 .

The X-ray tube 11 generates X-rays by applying a high voltage from the X-ray high voltage device 14 and irradiating thermal electrons from a cathode (filament) toward an anode (target). X-ray tube 11 includes a vacuum tube. For example, the X-ray tube 11 is a rotating anode type X-ray tube that generates X-rays by irradiating a rotating anode with thermal electrons.

The wedge 12 is a filter for adjusting the dose of X-rays irradiated from the X-ray tube 11 to the subject P. As shown in FIG. The wedge 12 attenuates the X-rays passing through itself so that the X-ray dose distribution irradiated from the X-ray tube 11 to the subject P becomes a predetermined distribution. The wedge 12 is also called a wedge filter or a bow-tie filter. The wedge 12 is, for example, processed aluminum so as to have a predetermined target angle and a predetermined thickness.

The collimator 13 is a mechanism for narrowing down the irradiation range of X-rays transmitted through the wedge 12 . The collimator 13 narrows down the X-ray irradiation range by, for example, forming a slit by combining a plurality of lead plates. The collimator 13 is sometimes called an X-ray diaphragm.

The X-ray high voltage device 14 has, for example, a high voltage generator and an X-ray controller. The high voltage generator has an electric circuit including a transformer, a rectifier, etc., and generates a high voltage to be applied to the X-ray tube 11 . The X-ray controller controls the output voltage of the high voltage generator in accordance with the amount of X-rays to be generated by the X-ray tube 11 . The high voltage generator may be one that boosts the voltage with the transformer described above, or one that boosts the voltage with an inverter.
The X-ray high voltage device 14 may be provided on the rotating frame 17 or may be provided on the fixed frame (not shown) side of the gantry device 10 .

The X-ray detector 15 detects the intensity of X-rays generated by the X-ray tube 11 and incident through the subject P. FIG. The X-ray detector 15 outputs to the DAS 18 an electrical signal (which may be an optical signal or the like) corresponding to the intensity of the detected X-rays. The X-ray detector 15 has, for example, multiple X-ray detection element arrays. Each of the plurality of X-ray detection element arrays has a plurality of X-ray detection elements arranged in the channel direction along an arc centered on the focal point of the X-ray tube 11 . A plurality of X-ray detection element arrays are arranged in a slice direction (column direction, row direction).

X-ray detector 15 is, for example, an indirect detector having a grid, a scintillator array, and a photosensor array. The scintillator array has a plurality of scintillators. Each scintillator has a scintillator crystal. The scintillator crystal emits an amount of light corresponding to the intensity of incident X-rays. The grid has an X-ray shielding plate arranged on the surface of the scintillator array on which X-rays are incident and having a function of absorbing scattered X-rays. Note that the grid may also be called a collimator (one-dimensional collimator or two-dimensional collimator). The photosensor array has photosensors such as, for example, photomultiplier tubes (photomultipliers: PMTs). The photosensor array outputs an electrical signal corresponding to the amount of light emitted by the scintillator. The X-ray detector 15 may be a direct conversion detector having a semiconductor element that converts incident X-rays into electrical signals.

DAS 16 has, for example, an amplifier, an integrator, and an A/D converter. The amplifier amplifies the electrical signal output from each X-ray detection element of the X-ray detector 15 . The integrator integrates the amplified electrical signal over a view period (described later). The A/D converter converts an electrical signal representing the result of integration into a digital signal. The DAS 16 outputs detection data based on digital signals to the console device 40 . Detected data is a digital value of x-ray intensity identified by the channel number of the x-ray detector element from which it was generated, the row number, and the view number indicating the acquired view. The view number is a number that changes according to the rotation of the rotating frame 17, and is a number that is incremented according to the rotation of the rotating frame 17, for example. Therefore, the view number is information indicating the rotation angle of the X-ray tube 11 . A view period is a period that falls between the rotation angle corresponding to a certain view number and the rotation angle corresponding to the next view number. The DAS 16 may detect the switching of the view by a timing signal input from the control device 18, by an internal timer, or by a signal obtained from a sensor (not shown). . When X-rays are continuously emitted from the X-ray tube 11 when performing a full scan, the DAS 16 collects detection data groups for the entire circumference (for 360 degrees). When X-rays are continuously emitted from the X-ray tube 11 when half scanning is performed, the DAS 16 collects detection data for a half circumference (180 degrees).

The rotating frame 17 is an annular member that supports the X-ray tube 11, the wedge 12, the collimator 13, and the X-ray detector 15 so as to face each other. The rotating frame 17 is rotatably supported by the stationary frame around the subject P introduced therein. Rotating frame 17 also supports DAS 16 . Detection data output by the DAS 16 is transmitted by optical communication from a transmitter having a light emitting diode (LED) mounted on the rotating frame 17 to a photodiode mounted on a non-rotating portion (e.g., fixed frame) of the gantry 10. It is transmitted to the receiver and transferred to the console device 40 by the receiver. The method of transmitting the detection data from the rotating frame 17 to the non-rotating portion is not limited to the above-described method using optical communication, and any non-contact transmission method may be employed. The rotating frame 17 is not limited to an annular member, and may be a member such as an arm as long as it can support and rotate the X-ray tube 11 or the like.

The X-ray CT apparatus 1 is, for example, a Rotate/Rotate-type X-ray CT apparatus (third Generation CT), but not limited to this, Stationary/Rotate-Type in which a plurality of annularly arranged X-ray detection elements are fixed to a fixed frame, and the X-ray tube 11 rotates around the subject P. It may be an X-ray CT device (fourth generation CT).

The control device 18 has, for example, a processing circuit having a processor such as a CPU (Central Processing Unit), and a driving mechanism including a motor, an actuator, and the like. The control device 18 receives an input signal from an input interface 43 attached to the console device 40 or the gantry device 10 and controls the operations of the gantry device 10 and the bed device 30 .

The control device 18 rotates the rotating frame 17, tilts the gantry device 10, and moves the top board 33 of the bed device 30, for example. When tilting the gantry device 10 , the control device 18 rotates the rotating frame 17 about an axis parallel to the Z-axis direction based on the tilt angle (tilt angle) input to the input interface 43 . The control device 18 grasps the rotation angle of the rotating frame 17 from the output of a sensor (not shown) or the like. The control device 18 also provides the rotation angle of the rotating frame 17 to the processing circuit 50 at any time. The control device 18 may be provided in the gantry device 10 or may be provided in the console device 40 .

The bed device 30 is a device on which the subject P to be scanned is placed, moved, and introduced into the rotating frame 17 of the gantry device 10 . The bed device 30 has, for example, a base 31 , a bed driving device 32 , a top board 33 and a support frame 34 . The base 31 includes a housing that supports the support frame 34 so as to be movable in the vertical direction (Y-axis direction). The bed driving device 32 includes motors and actuators. The bed driving device 32 moves the tabletop 33 on which the subject P is placed in the longitudinal direction (Z-axis direction) of the tabletop 33 along the support frame 34 . The top plate 33 is a plate-like member on which the subject P is placed.

The bed drive device 32 may move not only the top plate 33 but also the support frame 34 in the longitudinal direction of the top plate 33 . Contrary to the above, the gantry device 10 may be movable in the Z-axis direction, and the rotation frame 17 may be controlled to come around the subject P by moving the gantry device 10 . Moreover, both the gantry device 10 and the top plate 33 may be configured to be movable. Further, the X-ray CT apparatus 1 may be an apparatus in which the subject P is scanned in a standing or sitting position. In this case, the X-ray CT apparatus 1 has a subject support mechanism in place of the bed device 30, and the gantry device 10 rotates the rotating frame 17 about the axial direction perpendicular to the floor surface.

The console device 40 has, for example, a memory 41 , a display 42 , an input interface 43 , a memory 41 , a network connection circuit 44 and a processing circuit 50 . In the embodiment, the console device 40 is described as being separate from the gantry device 10 , but the gantry device 10 may include some or all of the components of the console device 40 .

The memory 41 is implemented by, for example, a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like. The memory 41 stores, for example, detection data, projection data, reconstructed images, CT images, and the like. These data may be stored in an external memory with which the X-ray CT apparatus 1 can communicate, instead of the memory 41 (or in addition to the memory 41). The external memory is controlled by the cloud server, for example, when the cloud server that manages the external memory receives a read/write request. The external memory is implemented, for example, by a system called PACS (Picture Archiving and Communication Systems). PACS is a system that systematically stores images and the like taken by various image diagnostic apparatuses.

FIG. 2 is a diagram showing an example of data stored in the memory 41. As shown in FIG. As shown in FIG. 2, the memory 41 stores, for example, an imaging condition 41-1, detection data 41-2 generated by the processing circuit 50, projection data 41-3, a reconstructed image 41-4, a scan range 41 -7, learned model 41-8, and other information is stored. The reconstructed image 41-4 can be classified into, for example, a scanogram 41-5 and a photographed image 41-6.

The display 42 displays various information. For example, the display 42 displays a medical image (CT image) generated by the processing circuit, a GUI (Graphical User Interface) image for receiving various operations by the operator, and the like. The display 42 is, for example, a liquid crystal display, a CRT (Cathode Ray Tube), an organic EL (Electroluminescence) display, or the like. The display 42 may be provided on the gantry device 10 . The display 42 may be of a desktop type, or may be a display device (for example, a tablet terminal) capable of wireless communication with the main body of the console device 40 .

The input interface 43 accepts various input operations by the operator and outputs an electrical signal indicating the content of the accepted input operation to the processing circuit 50 . For example, the input interface 43 accepts acquisition conditions for acquiring detection data or projection data (described later), reconstruction conditions for reconstructing CT images, image processing conditions for generating post-processed images from CT images, and the like. accepts the input operation of For example, the input interface 43 is implemented by a mouse, keyboard, touch panel, trackball, switch, button, joystick, camera, infrared sensor, microphone, and the like. The input interface 43 may be provided on the gantry device 10 . Also, the input interface 43 may be realized by a display device (for example, a tablet terminal) capable of wireless communication with the main body of the console device 40 .

The network connection circuit 44 includes, for example, a network card having a printed circuit board, or a wireless communication module. The network connection circuit 44 implements an information communication protocol according to the form of the network to be connected. Networks include, for example, LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, cellular networks, dedicated lines, and the like.

A processing circuit 50 controls the overall operation of the X-ray CT apparatus 1 . The processing circuit 50 may, for example,
A system control function 51, a preprocessing function 52, a reconstruction processing function 53, an image processing function 54, a scan control function 55, a display control function 56, a learning function 57, a transfer function 58, and the like are executed. The processing circuit 50 implements these functions by executing a program stored in the memory 41 by a hardware processor, for example.

A hardware processor is, for example, a CPU, a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (Simple Programmable Logic Device; SPLD) or Complex Programmable Logic Device (CPLD), Field Programmable Gate Array (FPGA)) and other circuits. Instead of storing the program in the memory 41, the program may be directly embedded in the circuitry of the hardware processor. In this case, the hardware processor realizes its function by reading and executing the program embedded in the circuit. The hardware processor is not limited to being configured as a single circuit, and may be configured as one hardware processor by combining a plurality of independent circuits to implement each function. Also, a plurality of components may be integrated into one hardware processor to realize each function.

Each component of the console device 40 or the processing circuit 50 may be distributed and realized by a plurality of pieces of hardware. The processing circuit 50 may be realized by a processing device that can communicate with the console device 40 instead of the configuration that the console device 40 has. The processing device is, for example, a workstation connected to one X-ray CT device, or a device connected to a plurality of X-ray CT devices and collectively executing processing equivalent to the processing circuit 50 described below (for example, cloud server).

The system control function 51 controls various functions of the processing circuit 50 based on input operations received by the input interface 43 .

A pre-processing function 52 performs pre-processing such as logarithmic conversion processing, offset correction processing, inter-channel sensitivity correction processing, and beam hardening correction on the detection data 41-2 output from the DAS 16 to obtain projection data 41-3. is generated, and the generated projection data 41-3 is stored in the memory 41. FIG.

The reconstruction processing function 53 performs reconstruction processing on the projection data generated by the preprocessing function 52 using a filtered back projection method, an iterative reconstruction method, or the like to generate a reconstructed image 41-4, The generated reconstructed image 41 - 4 is stored in the memory 41 .

The image processing function 54 converts the reconstructed image 41-4 into a three-dimensional image or cross-sectional image data of an arbitrary cross-section by a known method based on the input operation received by the input interface 43. FIG. Conversion to a three-dimensional image may be performed by preprocessing function 52 .

The scan control function 55 controls detection data collection processing in the gantry device 10 by instructing the X-ray high-voltage device 14 , the DAS 16 , the control device 18 , and the bed driving device 32 . The scan control function 55 controls the operation of each part when capturing the scan image 41-5 or the actual captured image 41-6 and when capturing an image used for diagnosis.

The display control function 56 controls the display mode of the display 42 .

A learning function 57 learns the scan range 41-7. Detailed processing of the learning function 57 will be described later. The learning function 57 is an example of a "learning unit".

A transfer function 58 transfers part or all of the captured reconstructed image 41-4 to the PACS. The transfer function 58 is an example of a "transfer section."

With the above configuration, the X-ray CT apparatus 1 scans the subject P in a manner such as helical scanning, conventional scanning, and step-and-shoot. Helical scanning is a mode in which the subject P is helically scanned by rotating the rotating frame 17 while moving the top plate 33 . Conventional scanning is a mode in which the subject P is scanned in a circular orbit by rotating the rotation frame 17 while the tabletop 33 is stationary. Run a conventional scan. Step-and-shoot is a mode in which the position of the top plate 33 is moved at regular intervals to perform conventional scanning in a plurality of scan areas.

FIG. 3 is a configuration diagram of the scan control function 55. As shown in FIG. The scan control function 55 includes, for example, a scanogram acquisition function 55-1, a scan range automatic setting function 55-2, a scan range manual setting function 55-3, and a scan execution function 55-4.

The scanogram acquisition function 55-1 acquires the scanogram 41-5 of the subject P for determining the scan range. Note that the scanogram is an example of an alignment image. Other examples of alignment images include scout images.

A method of acquiring a scanogram will be described. The scanogram acquisition function 55-1 causes the control device 18 to fix the position of the X-ray tube 11 at a predetermined rotation angle, and moves the top board 33 of the bed device 30 in the Z-axis direction while moving the X-ray tube 11. The rays are controlled to irradiate the subject P. The scanogram acquisition function 55-1 acquires a two-dimensional scanogram 41-5 of the subject P from the detection data 41-2 thus acquired.

The acquisition method of the scanogram 41-5 is not limited to the method described above. For example, a method of acquiring the projection data 41-3 for the entire circumference of the subject P by helical scanning or non-helical scanning may be used. good. Here, the scan image acquisition function 55-1 scans a wide range of subjects such as the entire chest, the entire abdomen, the entire upper body, and the whole body with a dose lower than that of the actual imaging. Non-helical scanning is, for example, step-and-shoot scanning. In this way, the scanogram acquisition function 55-1 collects the detection data 41-2 for the entire periphery of the subject P, so that the reconstruction processing function 53 can generate a three-dimensional reconstructed image 41-4 (volume data). may be reconstructed, and a two-dimensional scanogram 41-5 corresponding to an arbitrary direction may be generated based on the reconstructed volume data. The scanogram 41-5 can also be treated as a three-dimensional scanogram 41-5 by using the volume data described above.

The scanogram acquisition function 55-1 outputs the scanogram acquired by, for example, one of the methods exemplified above to the scan range automatic setting function 55-2. The scanogram image acquisition function 55-1 is an example of an “acquisition unit”. Note that the "acquisition unit" is not limited to "generating and acquiring by itself" as in the above example, but may be "acquiring from other functional units".

The scan range automatic setting function 55-2 inputs the scanogram image output by the scanogram acquisition function 55-1 and the imaging conditions to the learned model 41-8, and according to the output information obtained as a result, A scan range 41 - 7 is set and stored in the memory 41 .

The trained model 41-8 is trained by the learning function 57, for example, as will be described later. The trained model 41-8, for example, when the subject's imaging conditions (for example, age, sex, body type, disease name, scan target site, examination conditions, bed position conditions, some or all of them) are input, the scan range It is learned to output Sites to be scanned include, for example, the head, upper body, lungs, and liver. Examination conditions include, for example, initial imaging, follow-up imaging, and the like. In this embodiment, the imaging conditions are assumed to be input parameters. A matching trained model 41-8 may be selected and used in the above process. Furthermore, the learned model 41-8 for each first portion of the imaging conditions is learned, and the second portion of the imaging conditions is used as an input parameter. A trained model 41-8 that matches the first part of the imaging conditions may be selected and used in the above process.

FIG. 4 is a diagram for explaining the contents of processing by the scan range automatic setting function 55-2. The scan range automatic setting function 55-2 outputs the scan range 41-7 of the subject P by inputting the imaging conditions 41-1 and the scanogram 41-5 as parameters to the learned model 41-8. . By performing the subsequent processing based on this scan range 41-7, the scan planning time required from the scanogram imaging of the subject P to the actual imaging can be shortened compared to the case where the scan range is manually set from the beginning. can be shortened.

The scan range automatic setting function 55-2 causes the memory 41 to store the scan range 41-7 automatically set in this way. The scan range automatic setting function 55-2 is an example of a "processing unit".

Note that the scan range automatic setting function 55-2 omits the process of setting the scan range 41-7 when a suitable learned model 41-8 does not exist, and manually sets the scan range of the scan image 41-5. It may be output to function 55-3.

The scan range manual setting function 55-3 accepts fine adjustment, redo, new setting, etc. of the scan range by the operator. The scan range manual setting function 55-3, for example, reflects the details of fine adjustment input by the operator via the input interface 43 to the scan range 41-7 output by the scan range automatic setting function 55-2. and update the scan range 41-7 in the memory 41.

Further, the scan range manual setting function 55-3 redoes the scan range setting when the operator performs an operation indicating that the scan range 41-7 output by the scan range automatic setting function 55-2 is not adopted. (or create a new one). In this case, the scan range manual setting function 55-3 performs an operation input by the operator via the input interface 43 (for example, the display 42 Input operation for displaying a frame line indicating the scan range 41-7 above the scanogram 41-5, moving the position of the frame line, and enlarging or reducing the size of the frame line) , the scan range 41-7 is set accordingly. The scan range manual setting function 55-3 overwrites the scan range 41-7 stored in the memory 41 with the set scan range 41-7. Likewise, when the scan range automatic setting function 55-2 omits the scan range 41-7 setting process, the scan range manual setting function 55-3 indicates the scan range 41-7 on the scanogram 41-5. Input for displaying a frame line, moving the position of the frame line, enlarging or reducing the size of the frame line, or deleting an arbitrary ratio from the edge of the scanogram 41-5 to make it smaller. A scan range 41-7 is set according to the operation. The scan range manual setting function 55-3 causes the memory 41 to store the scan range 41-7 set by fine adjustment, redo, or the like. The scan range manual setting function 55-3 is an example of the "image processing section".

Note that the scan range automatic setting function 55-2 and the scan range manual setting function 55-3 may set a reconstruction range when reconstructing a CT image. In this case, the scan range automatic setting function 55-2, for example, performs a predetermined calculation for converting the scan range 41-7 output by the learned model 41-8 into a reconstruction range, and based on the calculation result, Cause the controller 18 to perform a scan. At this time, the scanning range 41-7 output by the trained model 41-8 may be wider than the reconstruction range. Further, when the scan range automatic setting function 55-2 and the scan range manual setting function 55-3 set the reconstruction range, and when there is a learned model outputting the reconstruction range in the trained model 41-8, A trained model that outputs its reconstruction range may be used. In this case, the scan range manual setting function 55-3 sets a range obtained by extending the reconstruction range vertically and horizontally as the scan range 41-7.

The scan execution function 55-4 causes the control device 18 to perform actual photography within the range defined by the scan range 41-7 stored in the memory 41, and to acquire the actual captured image 41-6 (scanned image). Control.

For the scan range 41-7, it is discriminated whether the scan range that is the result of automatic setting by the scan range automatic setting function 55-2 is adopted or whether manual setting is performed by the scan range manual setting function 55-3. A flag or the like to be set may be set.

The learning function 57 acquires a set of scanograms, imaging conditions, and scan ranges for a plurality of subjects from the memory 41 or an external device. The learning function 57 performs machine learning using acquired scanograms and imaging conditions including information about the scan target region as learning data, and scanograms set for the same subject as teacher data. Generate model 41-8. The learning function 57 is an example of a "model generator".

Also, the learning function 57 may be realized by an external device. In that case, the transfer function 58 may transfer at least the scanogram 41-5 to be transferred and the scan range 41-7 associated with the scanogram 41-5 to be used for learning. Further, the transfer function 58 may further transfer the reconstruction range of the reconstructed image 41-4 generated based on the scanogram 41-5 to be transferred and use it for learning.

FIG. 5 is a diagram for explaining the processing of the learning function 57. As shown in FIG. The learning function 57 inputs a plurality of sets of learning data to a machine learning model in which connection information etc. are defined in advance and parameters such as connection coefficients are provisionally set, and the results correspond to the learning data. Adjust the parameters in the machine learning model so that it approaches the teacher data that Imaging conditions and scanograms are input to the machine learning model.

For example, the imaging conditions described above are set as the imaging conditions input to the machine learning model.

The scanogram input to the machine learning model includes, for example, information set during the examination (for example, the range of reconstruction in the body axis direction, setting information of ROI (region of interest) at the time of image reconstruction), etc. you can

The learning function 57 adjusts the parameters of the machine learning model by, for example, back propagation. The machine learning model is, for example, a DNN (Deep Neural Network) using a CNN (Convolution Neural Network). Note that the machine learning model may perform weighting setting for each learning data so that newer learning data is more likely to be reflected as an output.

The learning function 57 completes the process after learning a predetermined number of sets of learning data and the corresponding teacher data. The machine learning model at that time is stored as a learned model 41-8.

A scanogram may be captured multiple times from the same or similar directions, such as when artifacts are detected and recapturing is required. Therefore, the learning function 57 sets only the scanogram (that is, the successful case) transferred by the transfer function 58 as the learning target, and the learned model reflects the result of the scan range setting processing by the scan control function 55 in the learned model. By updating , a more suitable trained model can be generated. In that case, the learning function 57 extracts the range acquired as the reconstructed image 41-4 from the scanogram image of the successful case transferred by the transfer function 58 and sets it as a learning target, thereby achieving more accurate learning. You may generate a ready-made model.

In addition, although a plurality of images acquired at two or more different angles may be used as scanograms used for scan planning, the learning function 57 may generate a trained model from some of the images. Alternatively, you can generate a trained model from all images.

The trained model 41 - 8 used by the learning function 57 or the learning data and teacher data used by the learning function 57 may be generated by another X-ray CT apparatus 1 .

6 and 7 are diagrams for explaining an example of the usage environment of the X-ray CT apparatus 1. FIG. A trained model generated by another X-ray CT apparatus 1 or a learning apparatus, or learning data and teacher data used by the learning function 57, for example, are sent to facility H1 using X-ray CT apparatus 1 as shown in FIG. provided by a service person MP or the like of the manufacturer of the X-ray CT apparatus 1 . Also, the learned model generated by another X-ray CT apparatus 1, or the learning data and teacher data used by the learning function 57 are, for example, available at facilities H1 to H1 of a plurality of X-ray CT apparatuses 1, as shown in FIG. HN (N is any natural number) may be shared by the cloud server C or the like via the external network NW. The cloud server C may exclusively perform learning similar to the learning function 57 .

FIG. 8 is a flowchart showing an example of the flow of imaging processing by the X-ray CT apparatus 1. As shown in FIG.

First, the console device 40 receives input of information specifying the subject P and imaging conditions (step S100), and performs settings for the imaging (for example, an operation to move the couch device 30 to the imaging start position of the tabletop 33). Accept (step S102). Next, the control device 18 controls the gantry device 10 to capture the scanogram 41-5 (step S104).

Next, the imaging conditions and the scanogram, which are the input parameters, are input to the learned model 41-8 (step S106), and the scan range 41-7, which is the output of the learned model, is displayed on the display 42 (step S108). Note that if there are multiple learned models 41-8 applied in step S106, multiple patterns of the scan range 41-7 may be displayed in step S108, and the most suitable learned model 41-8 may be displayed in step S106. may be selected.

Next, the input interface 43 selects whether to adopt the scan range 41-7 displayed in step S108 or not to adopt the scan range 41-7 and to set the scan range manually by the operator. 1 accepts the input of the judgment result by the operator (step S110). The scan range manual setting function 55-3 receives the scan range setting performed via the input interface 43 when the operator manually sets the scan range (step S112). When adopting the scanning range displayed in step S110, the process proceeds to step S114, which will be described later.

The scan range manual setting function 55-3 accepts the final fine adjustment by the operator's manual operation (step S114). Next, the scan execution function 55-4 carries out the actual photographing (step S116).

Next, the transfer function 58 transfers the photographed image 41-6, which is the photographed result, to the PACS or the like (step S118). Next, the learning function 57 generates a learned model 41-8 based on the scanogram and imaging conditions (eg, reconstruction range, etc.). Alternatively, the learning function 57 reflects the scanogram image and the imaging conditions in the learned model (step S120). Details of the processing in step S120 will be described later. This is the end of the description of this flowchart.

FIG. 9 is a flowchart showing an example of the flow of learning processing by the learning function 57. As shown in FIG. The flow of processing shown in FIG. 9 corresponds to the details of processing in step S120 of FIG. The flow chart of FIG. 9 may be performed each time the X-ray CT apparatus 1 completes imaging of one subject, or every time the scanning range is redone by manual operation by the operator. (every time step S112 is executed).

First, the learning function 57 acquires a scanogram and imaging conditions as one set of learning data (step S200). Next, the learning function 57 inputs the set of learning data acquired in step S200 to the machine learning model (step S202), and back-propagates errors from the teacher data corresponding to the set of learning data (step S204). .

Next, the learning function 57 determines whether or not the processing of steps S202 and S204 has been performed for a predetermined number of sets of learning data (step S206). If the processing of steps S202 and S204 has not been performed for the predetermined number of sets of learning data, the learning function 57 returns the processing to step S200. When the processing of steps S202 and S204 is performed for a predetermined number of sets of learning data, the learning function 57 determines the trained model 41-8 using the parameters at that time (step S208), and ends the processing of this flowchart. do. In step S206, if the processing cannot be continued because the predetermined number of sets of learning data does not exist, the subsequent processing may be reduced.

As described above, according to the X-ray CT apparatus 1 of the first embodiment described above, the scanogram acquiring function 55-1 for acquiring the information on the scan target region and the scanogram of the subject P, the information on the scan target region and the subject The information on the scan target site acquired by the scanogram acquisition function 55-1 and the image of the subject P are acquired for the trained model 41-8 that outputs the scan range of the subject at the time of scanning based on the scanogram of the subject. a scan range automatic setting function 55-2 for inputting a scan image and outputting a scan range 41-7 related to the subject P related to the acquired information about the scan target site and the scan image of the subject P. Accordingly, it is possible to easily adjust the imaging position of the subject at the time of imaging.

(Second embodiment)
The X-ray CT apparatus 1A of the second embodiment will be described below. In the following description, the same reference numerals as in the first embodiment are given to the same configurations and functions as in the first embodiment, and detailed description thereof will be omitted. Further, "A" is added to the end of the reference numerals for components that have the same names as those of the first embodiment but have different configurations or functions.

FIG. 10 is a diagram for explaining a gantry device 10A according to the second embodiment. The gantry device 10A further includes a camera 19 as compared with the gantry device 10 of the first embodiment. The camera 19 captures an image of the space including the subject P and acquires an optical image 41-9. The camera 19 may be installed at a place other than the gantry device 10, for example, a position where the entire bed device 30 can be imaged (eg, the ceiling of the room where the X-ray CT apparatus 1A is installed, or the vicinity of the bed device 30). ) may be installed.

FIG. 11 is a diagram showing an example of data stored in the memory 41A according to the second embodiment. Memory 41A additionally stores optical images 41-9 compared to memory 41 of the first embodiment.

FIG. 12 is a configuration diagram of a scan control function 55A according to the second embodiment. The scan control function 55A further comprises an optical image acquisition function 55-5 as compared with the scan control function 55 of the first embodiment.

The optical image acquisition function 55-5 acquires the optical image 41-9 captured by the camera 19 and the imaging conditions 41-1 associated with the subject P reflected in the optical image, and automatically sets the scan range. Output to 55-2A. The optical image acquisition function 55-5 is another example of the 'acquisition unit'.

The scan range automatic setting function 55-2A inputs the optical image output by the optical image acquisition function 55-5 and the shooting conditions to the learned model 41-8A, and according to the output information obtained as a result , a scan range 41-7 is set and stored in the memory 41. FIG.

The scan range automatic setting function 55-2A accepts scan range adjustment, redo, new setting, etc. by the operator. When the scan range automatic setting function 55-2A receives an operation indicating that the scan range is to be adopted, the actual photographing is performed after the details of the fine adjustment input by the operator via the input interface 43 are reflected. This fine adjustment is performed by the scan range manual setting function 55-3. When the scan range automatic setting function 55-2A receives processing indicating redo, new setting, etc., that is, when receiving processing indicating that the scan range based on the optical image is not adopted, the optical image is reacquired. Alternatively, processing for acquiring a scanogram image may be performed. The scan range automatic setting function 55-2A is another example of the 'processing section'.

FIG. 13 is a diagram for explaining the contents of processing by the scan range automatic setting function 55-2A. The scan range automatic setting function 55-2A scans the subject P by inputting the inspection condition information included in the imaging conditions 41-1 as parameters and the optical image 41-9 into the learned model 41-8. Output range 41-7. By performing the subsequent processing based on this scan range 41-7, it is possible to shorten the scan planning time required from the scanogram imaging of the subject P to the actual imaging.

The learning function 57A generates a learned model using the optical image and the shooting conditions as learning data, in addition to a learned model using the scanogram and the shooting conditions as learning data. The learning function 57A acquires a set of optical images, imaging conditions, and scan ranges for multiple subjects from the memory 41 or an external device. The learning function 57A performs machine learning using the acquired optical image and the imaging conditions including information about the scan target region as learning data, and the scanning range set for the same subject as teacher data. Generate model 41-8.

FIG. 14 is a diagram for explaining the processing of the learning function 57A. The learning function 57A inputs a plurality of sets of learning data to a machine learning model in which connection information and the like are defined in advance and parameters such as connection coefficients are provisionally set, and the results correspond to the learning data. Adjust the parameters in the machine learning model so that it approaches the teacher data that Shooting conditions and optical images are input to the machine learning model. The learning function 57A completes the process after learning a predetermined number of sets of learning data and the corresponding teacher data. The machine learning model at that time is stored as the learned model 41-8A.

Note that the generation of the scan range based on the optical image by the scan range automatic setting function 55-2A and the generation of the scan range based on the scanogram image may be used together. The scan range automatic setting function 55-2A, for example, sets the scan range in the X-axis direction based on the output of the learned model of the scanogram, and sets the scan range in the Y-axis direction based on the trained optical image as a substitute for the scanogram. Set based on model output. As a result, the exposure dose of the operator of the X-ray CT apparatus 1A and the subject P can be reduced.

FIG. 15 is a flow chart showing an example of the flow of imaging processing by the X-ray CT apparatus 1A. Note that steps S300 and S302 in the flowchart of FIG. 15 correspond to steps S100 and S102 in the flowchart of FIG. Also, steps S312 to S328 in the flowchart of FIG. 15 correspond to steps S104 to S120 in the flowchart of FIG.

First, the console device 40 receives input of information specifying the subject P and imaging conditions (step S300), and receives settings for the imaging (step S302). Next, the optical image acquisition function 55-5 acquires an optical image captured by the camera 19 (step S304). Next, the optical image is applied to the learned model (step S306), and the scan range based on the learned model is displayed on the display 42 (step S308). Note that if there are multiple trained models applied in step S306, multiple patterns of the scan range may be displayed in step S308, and the most suitable trained model may be selected in step S36.

Next, the input interface 43 accepts the input of the determination result by the operator of the X-ray CT apparatus 1A as to whether to adopt the scan range displayed in step S308 or to set the scan range by taking a scanogram ( step S310). If an operation indicating adoption of the displayed scan range is received, the process proceeds to step S322, which will be described later. When the control device 18 receives an operation indicating to capture a scanogram, it controls the gantry 10 to capture a scanogram (step S312).

Next, the scan range automatic setting function 55-2A inputs the imaging conditions and the scanogram as input parameters to the learned model (step S314), and displays the scan range that is the output of the learned model on the display 42 ( step S316). Note that if there are multiple trained models applied in step S314, multiple patterns of the scan range may be displayed in step S316, or the most suitable trained model may be selected in step S314.

Next, the input interface 43 accepts the input of the determination result by the operator of the X-ray CT apparatus 1A as to whether to adopt the scan range setting displayed in step S316 or to manually set the scan range again. (Step S318). The scan range automatic setting function 55-2A accepts the scan range setting performed via the input interface 43 when the operator manually sets the scan range again (step S320). If the scan range displayed in step S316 is adopted, the process proceeds to step S322, which will be described later.

The scan range automatic setting function 55-2A accepts the final fine adjustment by the operator's manual operation (step S322). Next, the control device 18 performs actual photography (step S324).

Next, the transfer function 58 transfers the photographed image, which is the photographed result, to the PACS or the like (step S326). Next, the learning function 57A generates a learned model including the scanogram image or the optical image and the imaging conditions. Alternatively, the learning function 57A reflects the scanogram image or the optical image and the shooting conditions in the learned model (step S328). Details of the processing in step S328 will be described later. This is the end of the description of this flowchart.

FIG. 16 is a flowchart showing an example of the flow of optical image learning processing by the learning function 57A. The combination of the flowchart shown in FIG. 9 and the flowchart shown in FIG. 16 corresponds to the details of the processing in step S328 of FIG. The flow chart of FIG. 16 may be performed each time the X-ray CT apparatus 1 completes the imaging of one subject, or each time the scanning range is redone by manual operation by the operator. (every time step S320 is performed).

First, the learning function 57A acquires an optical image and imaging conditions as one set of learning data (step S400). Next, the learning function 57A inputs one set of learning data to the machine learning model (step S402), and back-propagates errors from teacher data corresponding to the one set of learning data (step S404).

Next, the learning function 57A determines whether or not the processing of steps S402 and S404 has been performed for a predetermined number of sets of learning data (step S406). If the processing of steps S402 and S404 has not been performed for the predetermined number of sets of learning data, the learning function 57A returns the processing to step S400. When the processing of steps S402 and S404 is performed for the predetermined number of sets of learning data, the learning function 57A determines the learned model 41-8 using the parameters at that time (step S408), and ends the processing of this flowchart. do. In step S406, if the processing cannot be continued because the predetermined number of sets of learning data do not exist, the subsequent processing may be reduced.

According to the second embodiment described above, in addition to the same effects as the first embodiment, the scanning range automatic setting function 55-2A generates the scanning range based on the optical image, thereby enabling the first Compared to the embodiment, the possibility of reducing the number of times of scanning scan images can be increased, and the possibility of reducing the exposure dose of the operator of the X-ray CT apparatus 1A and the subject P can be increased.

The embodiment described above can be expressed as follows.
a storage for storing programs;
a processor;
By executing the program, the processor
Acquisition of information about the area to be scanned and an alignment image of the subject,
Aligning the acquired information about the scan target region and the subject with respect to a trained model that outputs the scan range of the subject at the time of scanning based on the information about the scan target region and the alignment image of the subject By inputting an image, outputting the acquired information on the scan target site and the scan range on the subject related to the alignment image of the subject;
A medical image diagnostic apparatus configured to:

According to at least one embodiment described above, the imaging condition information (41-1) including information on the scan target region and the images (41-4, 41-9) for specifying the scan range of the subject are Based on the acquisition unit (55-1, 55-5) to be acquired, the imaging condition information, and the image for specifying the scan range of the subject, the imaging condition is applied to the trained model (41-8) By having a processing unit (55-2, 55-2A) that inputs information and an image for specifying the scan range of the subject and outputs the scan range of the subject during the scan In addition, it is possible to easily adjust the imaging position of the subject at the time of imaging.

While several embodiments of the invention have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These embodiments can be embodied in various other forms, and various reductions, substitutions, and alterations can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, as well as the scope of the invention described in the claims and equivalents thereof.

DESCRIPTION OF SYMBOLS 1, 1A... X-ray CT apparatus 10, 10A... Mounting apparatus 11... X-ray tube 12... Wedge 13... Collimator 14... X-ray high-voltage apparatus 15... X-ray detector 16... Data acquisition system 17... Rotating frame 18... Control Device 19 Camera 30 Bed device 31 Base 32 Bed driving device 33 Top plate 34 Support frame 40 Console device 50 Processing circuit 51 System control function 52 Preprocessing function 53 Reconfiguration processing function 54 ... image processing functions 55, 55A ... scan control function 55-1 ... scano image acquisition functions 55-2, 55-2A ... scan range automatic setting function 55-3 ... scan range manual setting function 55-4 ... scan execution function 55- 5... Optical image acquisition function 56... Display control function 57, 57A... Learning function 58... Transfer function

Claims (11)

an acquisition unit that acquires imaging conditions including information about a region to be scanned and an alignment image of a subject;
The acquired scan target for a trained model that outputs a scan range corresponding to the scan target region of the subject at the time of scanning based on the imaging conditions including information on the scan target region and the alignment image of the subject. By inputting the imaging conditions including the information about the part and the alignment image of the subject, the scan target part of the subject related to the acquired imaging conditions including the information about the scan target part and the alignment image of the subject. a processing unit that outputs a scan range corresponding to
A medical image diagnostic device comprising: The alignment image is a scanogram,
The medical image diagnostic apparatus according to claim 1. An image processing unit that reduces the alignment image by deleting an arbitrary ratio of images from the ends of the alignment image so as to include the scan range.
The medical image diagnostic apparatus according to claim 1 or 2. The image processing unit processes at least one of the alignment images transferred by a transfer unit that transfers the alignment images of the subject to an external memory that systematically stores the alignment images.
The medical image diagnostic apparatus according to claim 3. The image processing unit redoes the setting of the scan range or adjusts the scan range with respect to the scan range output by the processing unit.
The medical image diagnostic apparatus according to claim 3 or 4. The trained model is generated by performing machine learning using the alignment image and the imaging conditions including information about the scan target region as learning data and using the scan range set for the same subject as teacher data. further comprising a learning unit for
The medical image diagnostic apparatus according to any one of claims 3 to 5. The learning unit updates the learned model based on the result of processing by the image processing unit.
The medical image diagnostic apparatus according to claim 6. The learned model outputs a scan range corresponding to the scan target region of the subject at the time of scanning based on imaging conditions including information about the scan target region, alignment images of the subject, and examination conditions of the subject. and
The processing unit inputs, to the learned model, the imaging conditions including the acquired information about the scan target region, the alignment image of the subject, and the inspection conditions of the subject . outputting a scan range corresponding to the scan target region of the subject related to the imaging condition including information about the scan target region and the alignment image of the subject;
The medical image diagnostic apparatus according to any one of claims 1 to 7. the computer
Acquiring imaging conditions including information about a region to be scanned and an alignment image of the subject,
The acquired scan for a trained model that outputs a scan range corresponding to the scan target region of the subject at the time of scanning, based on the imaging conditions including information about the scan target region and the alignment image of the subject. By inputting an imaging condition including information about a target region and an alignment image of the subject, the scan target region related to the subject based on the acquired imaging conditions including the information about the scan target region and the alignment image of the subject. which outputs the scan range corresponding to
Medical diagnostic imaging method. an acquisition unit that acquires imaging conditions including information about a scan target region and an optical image of a subject;
The acquired scan for a trained model that outputs a scan range corresponding to the scan target region of the subject at the time of scanning, based on imaging conditions including information about the scan target region and an optical image of the subject. By inputting imaging conditions including information about a target site and an optical image of the subject, the scan target site related to the subject based on the acquired imaging conditions including the information about the scan target site and the optical image of the subject is obtained. a processing unit that outputs a corresponding scan range;
A medical image diagnostic device comprising: an acquisition unit that acquires imaging conditions including information about a region to be scanned and an alignment image of a subject;
For a trained model that outputs a reconstruction range corresponding to a scan target region of an image acquired by scanning based on imaging conditions including information about the scan target region and the alignment image of the subject, the acquired By inputting an imaging condition including information about a scan target region and an alignment image of the subject, the scan related to the subject based on the acquired imaging conditions including the information about the scan target region and the alignment image of the subject a processing unit that outputs a reconstruction range corresponding to a target part ;
A medical image diagnostic device comprising:

Images