Medical information processing apparatus, medical information processing method and program

Abstract

To facilitate grasping a relation among a pretreatment state, a treatment condition, and a treatment effect estimated under the treatment condition.SOLUTION: A medical information processing apparatus according to an embodiment includes an acquisition unit, an identification unit, an estimation unit, a determination unit, and a display control unit. The acquisition unit acquires an organ model in which a target organ for treatment is expressed, based on a medical image. The identification unit identifies a treatment condition which represents a treatment-related condition including at least a treatment position in the organ model. The estimation unit estimates a post-treatment feature quantity based on the organ model and the treatment position. The determination unit determines a display condition regarding the treatment position in the organ model based on the estimated feature quantity. The display control unit displays the organ model on a display device according to the determined display condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a medical information processing device, a medical information processing method, and a program.

[0002] Various treatment methods have been proposed for treating heart diseases. For example, a treatment method using a cardiac valve repair device using a catheter is known as a treatment method for diseases related to heart valves such as the mitral valve.

[0003] Known cardiac valve repair devices include, for example, mitral valve repair devices used to treat secondary mitral regurgitation (MR). Mitral valve repair devices are used in a procedure called Edge-to-Edge Repair, which increases the coaptation area by grasping the tips of the anterior and posterior leaflets of the mitral valve.

[0004] For example, when treatment is performed using a mitral valve repair device, before actually starting treatment, an estimation process may be performed to estimate post-treatment features (e.g., valve orifice area, regurgitation amount, etc.) that can estimate the treatment effect if treatment is performed under multiple treatment conditions (e.g., multiple gripping positions of the mitral valve repair device).

[0005] Conventionally, in the above-described scenario, an estimated value of the feature quantity after treatment for each treatment condition is provided to the user. Therefore, when estimation processing is performed for a plurality of treatment conditions, a medical professional such as a doctor estimates the treatment effect for each condition by comparing the estimated values ​​of the feature quantity after treatment for each condition, and considers the treatment condition.

[0006] However, with the above-described method, for example, when comparing and examining multiple treatment conditions, it may be difficult for medical professionals to grasp the relationship between the condition before treatment, the treatment conditions, and the estimated treatment effect under those treatment conditions. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Special Publication No. 2012-521863 Summary of the Invention [Problem to be solved by the invention]

[0008] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to facilitate understanding of the relationship between the pre-treatment state, treatment conditions, and the estimated treatment effect under those treatment conditions. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0009] A medical image processing device according to an embodiment includes an acquisition unit, an identification unit, an estimation unit, a determination unit, and a display control unit. The acquisition unit acquires an organ model representing an organ to be treated based on a medical image. The identification unit identifies treatment conditions indicating conditions related to treatment including at least the treatment position in the organ model. The estimation unit estimates post-treatment feature amounts based on the organ model and the treatment position. The determination unit determines display conditions for the treatment position in the organ model based on the estimated feature amounts. The display control unit displays the organ model on a display device in accordance with the determined display conditions. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a medical information processing system according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing an example of the configuration of the X-ray CT apparatus according to the embodiment. [Figure 3] FIG. 3 is a diagram showing an example of the definition of an organ model (mitral valve mesh) according to the embodiment. [Figure 4] FIG. 4 is a diagram showing another example of the definition of an organ model (mitral valve mesh) according to the embodiment. [Figure 5] FIG. 5 is a flowchart showing an example of processing executed by the medical image processing apparatus according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of a treatment position setting process according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of a treatment condition setting process according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of a display condition setting process according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of additional setting processing of a treatment position according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the display condition update process according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of setting display conditions for an organ model according to the second modification. [Figure 12] FIG. 12 is a diagram illustrating an example of setting display conditions for an organ model according to the second modification. [Figure 13] FIG. 13 is a diagram illustrating an example of setting display conditions for an organ model according to the second modification. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of a medical information processing device, a medical information processing method, and a program will be described in detail with reference to the accompanying drawings.

[0012] In this embodiment, a medical information processing system S including an X-ray CT apparatus 1 and a medical information processing device 2 will be described as an example, as shown in Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of the medical information processing system S according to the embodiment. In this embodiment, the description will be made assuming that each process described below is executed based on projection data collected by the X-ray CT apparatus 1 shown in Fig. 1. The X-ray CT apparatus 1 and the medical information processing device 2 are connected to each other via a network NW.

[0013] Note that the X-ray CT apparatus 1 and the medical information processing apparatus 2 may be installed in any location as long as they can be connected via the network NW. For example, the X-ray CT apparatus 1 and the medical information processing apparatus 2 may be installed in different facilities. That is, the network NW may be configured as a closed local network within the facility, or may be a network via the Internet.

[0014] Furthermore, communication between the X-ray CT apparatus 1 and the medical information processing apparatus 2 may be performed via another device such as an image storage device, or may be performed directly without going through another device. An example of such an image storage device is a PACS (Picture Archiving and Communication System) server.

[0015] First, the X-ray CT apparatus 1 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the X-ray CT apparatus 1 according to the embodiment. As shown in Fig. 2, the X-ray CT apparatus 1 includes a gantry device 10, a bed device 30, and a console device 40.

[0016] In this embodiment, the longitudinal direction of the rotation axis of the rotating frame 13 in the non-tilted state is defined as the Z-axis direction, the direction perpendicular to the Z-axis direction and extending from the center of rotation toward the support pillar supporting the rotating frame 13 is defined as the X-axis, and the direction perpendicular to the Z-axis and the X-axis is defined as the Y-axis.

[0017] The gantry device 10 has an imaging system 19 for capturing medical images used for diagnosis. The imaging system 19 is composed of, for example, an X-ray tube 11, an X-ray detector 12, a wedge 16, and a collimator 17. In other words, the gantry device 10 is an apparatus having the imaging system 19 that irradiates X-rays onto a subject P and collects projection data from detection data of the X-rays that have passed through the subject P.

[0018] The gantry device 10 also has an opening for accommodating the subject P. The tabletop 33 on which the subject P is placed is accommodated in the opening with the side where the bed device 30 is provided serving as an entrance.

[0019] The gantry device 10 has an X-ray tube 11, a wedge 16, a collimator 17, an X-ray detector 12, an X-ray high voltage device 14, a DAS (Data Acquisition System) 18, a rotating frame 13, a control device 15, and a bed device 30.

[0020] The X-ray tube 11 is a vacuum tube that irradiates thermoelectrons from a cathode (filament) toward an anode (target) by applying high voltage from the X-ray high voltage device 14. For example, the X-ray tube 11 may be a rotating anode type X-ray tube that generates X-rays by irradiating a rotating anode with thermoelectrons.

[0021] The wedge 16 is a filter for adjusting the amount of X-rays irradiated from the X-ray tube 11. Specifically, the wedge 16 is a filter that transmits and attenuates the X-rays irradiated from the X-ray tube 11 so that the X-rays irradiated from the X-ray tube 11 to the subject P have a predetermined distribution.

[0022] The wedge 16 is, for example, a wedge filter or a bow-tie filter, which is a filter made of aluminum processed to have a predetermined target angle and a predetermined thickness.

[0023] The collimator 17 is a lead plate or the like for narrowing down the irradiation range of the X-rays transmitted through the wedge 16, and a slit is formed by combining a plurality of lead plates or the like. The collimator 17 is also sometimes called an X-ray aperture.

[0024] The X-ray detector 12 detects X-rays emitted from the X-ray tube 11 and passing through the subject P, and outputs an electrical signal corresponding to the X-ray dose to a data acquisition system (DAS 18). The X-ray detector 12 has, for example, multiple X-ray detection element rows in which multiple X-ray detection elements are arranged in a channel direction along an arc centered on the focal point of the X-ray tube 11. The channel direction means the circumferential direction of the rotating frame 13.

[0025] The X-ray detector 12 has, for example, a plurality of X-ray detection element rows in which a plurality of X-ray detection elements are arranged in the channel direction along one arc centered on the focal point of the X-ray tube 11. The X-ray detector 12 has a structure in which, for example, a plurality of X-ray detection element rows in which a plurality of X-ray detection elements are arranged in the channel direction are arranged in the slice direction (also called the body axis direction or row direction).

[0026] The X-ray detector 12 is an indirect conversion detector having, for example, a grid, a scintillator array, and a photosensor array. The scintillator array has multiple scintillators, and the scintillators have scintillator crystals that output light with a photon amount corresponding to the amount of incident X-rays. The grid is arranged on the X-ray incident side of the scintillator array and has an X-ray shielding plate that has the function of absorbing scattered X-rays.

[0027] The photosensor array has a function of converting the amount of light from the scintillator into an electrical signal corresponding to the amount of light, and includes photosensors such as photomultiplier tubes (PMTs).The X-ray detector 12 may be a direct conversion type detector having a semiconductor element that converts incident X-rays into an electrical signal.

[0028] The X-ray high voltage device 14 has electric circuits such as a transformer and a rectifier, and includes a high voltage generator having a function of generating a high voltage to be applied to the X-ray tube 11, and an X-ray control device that controls the output voltage according to the X-rays irradiated by the X-ray tube 11. The high voltage generator may be of a transformer type or an inverter type.

[0029] The X-ray high voltage device 14 may be provided on the rotating frame 13, or may be provided on the fixed frame (not shown) side of the gantry device 10. The fixed frame is a frame that supports the rotating frame 13 so that it can rotate.

[0030] The DAS 18 has an amplifier that amplifies the electrical signals output from each X-ray detection element of the X-ray detector 12 and an A / D converter that converts the electrical signals into digital signals, and generates detection data. The detection data generated by the DAS 18 is transferred to the console device 40. The detection data is, for example, a sinogram.

[0031] A sinogram is data showing projection data generated for each X-ray detection element at each position (hereinafter also referred to as view angle) of the X-ray tube 11 in association with a view direction and a channel direction. Here, the view direction corresponds to the view angle and means the direction of X-ray irradiation.

[0032] When a single scan is performed using only one detector element row in the X-ray detector 12, one sinogram can be generated for one scan. When a helical scan or a volume scan is performed using multiple detector element rows in the X-ray detector 12, multiple sinograms can be generated for one scan.

[0033] The rotating frame 13 is an annular frame that supports the X-ray tube 11 and the X-ray detector 12 so that they face each other, and rotates the X-ray tube 11 and the X-ray detector 12 using a control device 15. In addition to the X-ray tube 11 and the X-ray detector 12, the rotating frame 13 also supports an X-ray high voltage device 14 and a DAS 18.

[0034] The rotating frame 13 is rotatably supported by a non-rotating part of the gantry (for example, a fixed frame, not shown in FIG. 2). The rotation mechanism includes, for example, a motor that generates a rotational driving force and a bearing that transmits the rotational driving force to the rotating frame 13 to rotate it. The motor is provided, for example, in the non-rotating part, and the bearing is physically connected to the rotating frame 13 and the motor, and the rotating frame 13 rotates in response to the rotational force of the motor.

[0035] The rotating frame 13 and the non-rotating part are each provided with a non-contact or contact communication circuit, which enables communication between the unit supported by the rotating frame 13 and the non-rotating part or an external device of the gantry 10.

[0036] For example, when optical communication is adopted as a non-contact communication method, the detection data generated by DAS18 is transmitted by optical communication from a transmitter having a light-emitting diode (LED) provided on the rotating frame 13 to a receiver having a photodiode provided on the non-rotating part of the platform device, and is further transferred from the non-rotating part to the console device 40 by the transmitter.

[0037] As a communication method, in addition to non-contact data transmission such as capacitive coupling or radio wave, a contact data transmission method using a slip ring and electrode brush may also be used.

[0038] The control device 15 has a processing circuit including a CPU etc. and a driving mechanism including a motor and an actuator etc. The control device 15 has a function of receiving an input signal from an input interface 43 (described later) attached to the console device 40 or the gantry device 10 and controlling the operation of the gantry device 10 and the bed device 30.

[0039] For example, upon receiving an input signal, the control device 15 performs control to rotate the rotating frame 13, control to tilt the gantry device 10, and control to operate the bed device 30 and the tabletop 33. Note that the control to tilt the gantry device 10 is realized by the control device 15 rotating the rotating frame 13 around an axis parallel to the X-axis direction based on inclination angle (tilt angle) information input through an input interface attached to the gantry device 10.

[0040] The control device 15 may be provided in the gantry device 10 or in the console device 40.

[0041] The bed device 30 is a device on which the subject P to be scanned is placed and moved, and includes a base 31, a bed driving device 32, a top plate 33, and a support frame 34. The base 31 is a housing that supports the support frame 34 so that it can move in the vertical direction. The bed driving device 32 is a motor or actuator that moves the top plate 33, on which the subject P is placed, in its longitudinal direction (the Z-axis direction in FIG. 2).

[0042] The tabletop 33 provided on the upper surface of the support frame 34 is a plate on which the subject P is placed. The bed driving device 32 may move the support frame 34 in the longitudinal direction of the tabletop 33 in addition to the tabletop 33.

[0043] The bed driving device 32 moves the base 31 in the vertical direction in accordance with a control signal from the control device 15. The bed driving device 32 also moves the tabletop 33 in the longitudinal direction (Z-axis direction) in accordance with a control signal from the control device 15.

[0044] The console device 40 is a device that accepts operations of the X-ray CT device 1 by an operator and reconstructs X-ray CT image data from the X-ray detection data collected by the gantry device 10. The console device 40 includes a memory 41, a display 42, an input interface 43, and a processing circuit 45.

[0045] The memory 41 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory 41 stores, for example, projection data and reconstructed image data. The memory 41 also stores an imaging protocol.

[0046] Here, the imaging protocol defines procedures for controlling the imaging system 19 to image the subject P and acquire images. The imaging protocol is a group of parameters such as the imaging region, imaging conditions, imaging range, reconstruction conditions, operation of the gantry device 10 (imaging system 19), operation of the bed device 30, etc.

[0047] The memory 41 also stores dedicated programs for implementing a system control function 451, a preprocessing function 452, a reconstruction processing function 453, and an image processing function 454, which will be described later.

[0048] The display 42 is a monitor that the operator refers to and displays various types of information. For example, the display 42 outputs medical images (CT images) generated by the processing circuitry 45, a GUI (Graphical User Interface) for receiving various operations from the operator, and the like. For example, the display 42 is a liquid crystal display or a CRT (Cathode Ray Tube) display.

[0049] The input interface 43 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuitry 45. For example, the input interface 43 receives from the operator acquisition conditions for collecting projection data, reconstruction conditions for reconstructing a CT image, image processing conditions for generating a post-processed image from the CT image, and the like.

[0050] Furthermore, for example, the input interface 43 is realized by a mouse, a keyboard, a trackball, a switch, a button, a joystick, etc. Furthermore, the input interface 43 may be provided in the gantry device 10. Furthermore, the input interface 43 may be configured as a tablet terminal or the like capable of wireless communication with the console device 40 main body.

[0051] The processing circuitry 45 controls the overall operation of the X-ray CT apparatus 1. The processing circuitry 45 has, for example, a system control function 451, a preprocessing function 452, a reconstruction processing function 453, and an image processing function 454.

[0052] In this embodiment, each processing function performed by the components of the system control function 451, the preprocessing function 452, the reconstruction processing function 453, and the image processing function 454 is stored in the form of a computer-executable program in the memory 41. The processing circuitry 45 is a processor that reads out the program from the memory 41 and executes it to realize the function corresponding to each program.

[0053] In other words, the processing circuit 45 in the state where each program has been read out has each function shown in the processing circuit 45 of FIG.

[0054] In FIG. 2, it has been described that the processing functions performed by the system control function 451, the preprocessing function 452, the reconstruction processing function 453, and the image processing function 454 are realized by a single processing circuit 45. However, it is also possible to configure the processing circuit 45 by combining a plurality of independent processors, and realize the functions by each processor executing a program.

[0055] In other words, each of the above functions may be configured as a program and one processing circuit may execute each program, or a specific function may be implemented in a dedicated, independent program execution circuit.

[0056] The system control function 451 controls various functions of the processing circuitry 45 based on an input operation received from an operator via the input interface 43. For example, the system control function 451 receives input of user information (e.g., a user ID, etc.) for login, subject information, etc. via the input interface 43. In addition, for example, the system control function 451 receives input of an imaging protocol via the input interface 43.

[0057] The pre-processing function 452 generates data by performing pre-processing such as logarithmic conversion processing, offset processing, sensitivity correction processing between channels, beam hardening correction, etc. on the detection data output from the DAS 18. Note that the data before pre-processing (detection data) and the data after pre-processing may be collectively referred to as projection data.

[0058] The reconstruction processing function 453 performs reconstruction processing using a filtered back projection method, an iterative reconstruction method, or the like on the projection data generated by the preprocessing function 452 in accordance with the reconstruction conditions, to generate a plurality of slice image data (CT image data). Note that the data before preprocessing (detection data and data after preprocessing) may also be collectively referred to as projection data.

[0059] The image processing function 454 converts the CT image data generated by the reconstruction processing function 453 into tomographic image data of an arbitrary cross section or three-dimensional image data by a known method based on an input operation received from the operator via the input interface 43. Note that the generation of three-dimensional image data may be performed directly by the reconstruction processing function 453.

[0060] The post-processing may be performed by either the console device 40 or the medical information processing device 2. The post-processing may also be performed by both the console device 40 and the medical information processing device 2 simultaneously.

[0061] The post-processing defined here is a concept that refers to processing of multiple slice image data generated by the pre-processing function 452. For example, the post-processing includes processing such as noise removal, multi-planar reconstruction (MPR) display of multiple slice image data, and rendering of volume data.

[0062] 1, the medical information processing device 2 will be described. The medical information processing device 2 is a device that executes a process of acquiring an organ model of a target organ to be treated based on slice image data generated by scanning a subject P with the X-ray CT device 1, estimating feature quantities related to the target organ after treatment, and displaying the estimation results on the organ model.

[0063] In this embodiment, as an example, a mitral valve mesh (organ model) for a patient with mitral regurgitation is used to estimate features related to the shape of the mitral valve after Edge to Edge Repair (e.g., valve orifice area during cardiac systole), and the estimation results are displayed on the organ model.

[0064] The medical information processing device 2 includes, for example, a memory 21, a display 22, an input interface 23, and a processing circuit 24, as shown in FIG.

[0065] The memory 21 stores various types of information. For example, the memory 21 stores setting information related to display settings related to feature amounts. Also, for example, the memory 21 stores programs that enable circuits included in the medical information processing device 2 to realize their functions. Also, for example, the memory 21 stores data received from the X-ray CT device 1 and data generated by the processing circuitry 24.

[0066] The memory 21 is realized by a semiconductor memory element such as a RAM or a flash memory, a hard disk, an optical disk, etc. The memory 21 may also be realized by a group of servers (cloud) connected to the medical information processing device 2 via a network NW.

[0067] The display 22 displays various types of information. For example, the display 22 displays a GUI for receiving various instructions, settings, etc. from a user via the input interface 23. Also, for example, the display 22 displays an organ model under the control of the processing circuitry 24. Also, for example, the display 22 displays estimated post-treatment features on the organ model.

[0068] Here, the display 22 is a liquid crystal display, a CRT display, etc. The display 22 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the medical information processing device 2 main body.

[0069] The input interface 23 accepts various input operations from the user, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 24. For example, the input interface 23 may be implemented by a mouse, keyboard, trackball, switch, button, joystick, a touchpad that performs input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, a voice input circuit, or the like.

[0070] The input interface 23 may be configured as a tablet terminal or the like capable of wireless communication with the main body of the medical information processing device 2. The input interface 23 may also be a circuit that accepts input operations from a user by motion capture. For example, the input interface 23 can accept the user's body movements, line of sight, etc. as input operations by processing signals acquired via a tracker and images collected about the user.

[0071] Furthermore, the input interface 23 is not limited to one equipped with physical operation components such as a mouse, a keyboard, etc. For example, an example of the input interface 23 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the medical information processing device 2 and outputs this electrical signal to the processing circuit 24.

[0072] The processing circuitry 24 controls the operation of the entire medical information processing device 2 by executing a first acquisition function 241, a second acquisition function 242, a first setting function 243, a second setting function 244, an estimation function 245, a decision function 246, a judgment function 247, and a display control function 248.

[0073] Here, the first acquisition function 241 and the second acquisition function 242 are an example of an acquisition unit. Furthermore, the first setting function 243 and the second setting function 244 are an example of an identification unit. Furthermore, the estimation function 245 is an example of an estimation unit. The determination function 246 is an example of a reception unit and a determination unit. Furthermore, the display control function 248 is an example of a display control unit.

[0074] 1, each processing function is stored in the form of a computer-executable program in the memory 21. The processing circuitry 24 is a processor that realizes the function corresponding to each program by reading and executing the program from the memory 21. In other words, the processing circuitry 24 in a state in which a program has been read has the function corresponding to the read program.

[0075] 1 has been described as realizing the first acquisition function 241, the second acquisition function 242, the first setting function 243, the second setting function 244, the estimation function 245, the decision function 246, the determination function 247, and the display control function 248 by a single processing circuit 24, but the processing circuit 24 may be configured by combining a plurality of independent processors, and each processor may execute a program to realize the functions. Furthermore, each processing function of the processing circuit 24 may be realized by being distributed or integrated as appropriate in a single or multiple processing circuits.

[0076] The processing circuitry 24 may also realize its functions by using a processor of an external device connected via a network NW. For example, the processing circuitry 24 reads and executes a program corresponding to each function from the memory 21, and realizes each function shown in FIG. 1 by using a group of servers (cloud) connected to the medical information processing device 2 via the network NW as a computational resource.

[0077] The first acquisition function 241 acquires a medical image. For example, the first acquisition function 241 acquires slice image data generated by scanning the subject P with the X-ray CT apparatus 1 as the medical image via the network NW.

[0078] In addition, when the medical information processing system S includes an image storage device, the first acquisition function 241 may acquire slice image data from the image storage device such as a PACS server. For example, the first acquisition function 241 may monitor the PACS server or the like via the network NW, and acquire new slice image data when the new slice image data is stored.

[0079] The second acquisition function 242 acquires an organ model that represents the target organ.

[0080] For example, the second acquisition function 242 acquires coordinate information of each pixel corresponding to a region of interest that represents a region (organ) to be treated in the slice image data. Specifically, the second acquisition function 242 receives a designation input of the region of interest from a user, and identifies the region of interest in the medical image according to the input. Then, the second acquisition function 242 acquires coordinate information of each pixel corresponding to the region of interest based on the identified region of interest.

[0081] The second acquisition function 242 may identify the region of interest based on an anatomical structure extracted from the slice image data by an existing region extraction method, such as Otsu's binarization method based on CT values, region growing method, snake algorithm, graph cut algorithm, and mean shift algorithm.

[0082] The second acquisition function 242 may also identify the region of interest using a shape model generated by an existing machine learning technique (including deep learning).

[0083] In this case, for example, the second acquisition function 242 applies a shape model capable of extracting coordinate information of a plurality of pixels corresponding to a region of interest in the slice image data to the slice image data acquired by the first acquisition function 241. Then, the second acquisition function 242 identifies the region of interest based on the extraction result obtained by applying the shape model.

[0084] In this case, the shape model is a trained model that uses, for example, known machine learning techniques (including deep learning techniques) to learn the relationship between the two using a dataset in which slice image data on which a region of interest is drawn is used as input training data, and coordinate information of multiple pixels corresponding to the region of interest in the slice image data is used as output training data.

[0085] Alternatively, for example, the second acquisition function 242 may receive a selection input from the user as to which of a plurality of region extraction methods to use for region extraction, and identify the region of interest using the selected region extraction method. The identified region of interest is represented, for example, by a three-dimensional mesh model.

[0086] Here, the three-dimensional mesh model is, for example, a region of interest that is expressed as a computational grid (hereinafter also referred to as a mesh) by setting a plurality of grid points on the identified region of interest. In this case, the number and arrangement of the grid points may be determined in advance, or may be determined based on the size, shape, etc. of the region of interest.

[0087] The method of expressing the region of interest is not limited to this, and any method may be used. For example, the region of interest may be expressed by a surface model.

[0088] The region of interest may be two-dimensional or three-dimensional. For example, when the first acquisition function 241 acquires four-dimensional images captured at multiple cardiac phases, the second acquisition function 242 may identify the region of interest for each image at each cardiac phase and acquire the region of interest as a four-dimensional organ model including information on time-series morphological changes of the target organ.

[0089] An organ model will be described below using a three-dimensional mesh model of a mitral valve (hereinafter also referred to as a mitral valve mesh) as an example. Fig. 3 is a diagram showing an example of the definition of the mitral valve mesh MM.

[0090] In the example of Figure 3, the group of grid points representing the mitral valve are expressed by the X coordinate in the direction from the annulus side to the valve cusp side, with 0 being on the annulus. Also, the Y coordinate in the circumferential direction of the annulus is expressed by the Y coordinate, with 0 being between the anterior and posterior leaflets. The mitral valve mesh MM in Figure 3 represents the mitral valve with 378 grid points in 9 rows and 42 columns located at positions (0,0) to (8,41) in the (X,Y) coordinate system.

[0091] When the region of interest is made up of multiple structures, different formats may be defined for each of the multiple structures in the organ model.

[0092] For example, since the mitral valve is composed of an anterior leaflet and a posterior leaflet, the anterior leaflet and the posterior leaflet may be displayed in different colors or with different line types. Below, a mitral valve mesh MM in which different types are defined for the anterior leaflet and the posterior leaflet will be described with reference to FIG. 4. FIG. 4 is a diagram showing an example of the definition of the mitral valve mesh MM. In the example of FIG. 4, the mitral valve mesh MM includes an anterior leaflet mesh AM and a posterior leaflet mesh PM.

[0093] The anterior leaflet mesh AM represents the region corresponding to the anterior leaflet of the mitral valve. In FIG. 4, the anterior leaflet mesh AM is composed of 171 lattice points arranged in 9 rows and 19 columns. Here, in FIG. 4, the position indicated by O1 is set as the origin (AM(0,0)) of the anterior leaflet mesh AM. Also, in FIG. 4, the curve direction (column direction) indicated by arrow X1 is defined as the X direction in the anterior leaflet mesh AM, and the curve direction (row direction) indicated by arrow Y1 is defined as the Y direction in the anterior leaflet mesh AM.

[0094] The posterior leaflet mesh PM represents the region corresponding to the posterior leaflet of the mitral valve. In FIG. 4, the posterior leaflet mesh PM is composed of 225 lattice points arranged in 9 rows and 25 columns. Here, in FIG. 4, the position indicated by O2 is set as the origin (PM(0,0)) of the posterior leaflet mesh PM. Also, in FIG. 4, the curve direction (column direction) indicated by arrow X2 is defined as the X direction in the posterior leaflet mesh PM, and the curve direction (row direction) indicated by arrow Y2 is defined as the Y direction in the posterior leaflet mesh PM.

[0095] In addition, in FIG. 4, the grid points (AM(0,0) to AM(8,0)) on one end of the anterior leaflet mesh AM are shared with the grid points (PM(0,24) to PM(8,24)) on one end of the posterior leaflet mesh PM. In addition, the grid points (AM(0,18) to AM(8,18)) on the other end of the anterior leaflet mesh AM are shared with the grid points (PM(0,0) to PM(8,0)) on the other end of the posterior leaflet mesh PM. Therefore, as in the example of FIG. 3, the mitral valve as a whole has a grid point group of 378 points arranged in 9 rows and 42 columns.

[0096] Returning to Fig. 1, the explanation will be continued. The first setting function 243 sets first treatment conditions for the treatment technique to be estimated. In this embodiment, the first treatment conditions are conditions related to the treatment device among the conditions used as conditions for the estimation process executed by the estimation function 245 described below.

[0097] For example, the first setting function 243 sets the size and type of treatment device to be used for Edge to Edge Repair for mitral valve regurgitation. The following describes an example in which the type of treatment device is selected from three types: MitraClip (registered trademark), PASCAL (Edwards Lifesciences), and DragonFly (Hangzhou Valgen Medtech Co. Ltd.).

[0098] In this example, for example, the display control function 248 described below displays a drop-down list on the display 22 from which one of three types of therapeutic devices can be selected: MitraClip (registered trademark), PASCAL (Edwards Lifesciences), and DragonFly (Hangzhou Valgen Medtech Co. Ltd.). The first setting function 243 receives a selection input of the type of therapeutic device from the user via the drop-down list, and sets the received type of therapeutic device as the type of therapeutic device.

[0099] Next, the display control function 248 displays a drop-down list from which the size of the treatment device can be selected according to the type of the treatment device that has been set on the display 22. The first setting function 243 receives a selection input of the size of the treatment device via the drop-down list, and sets the received size of the treatment device as the size of the treatment device.

[0100] As an example, if MitraClip (registered trademark) is set as the type of treatment device, the display control function 248 displays a drop-down list on the display 22 from which the clip width can be selected from two options, 4 mm and 6 mm, and the clip length can be selected from two options, 9 mm and 12 mm.

[0101] The first setting function 243 may acquire morphological information of the target organ from the organ model acquired by the second acquisition function 242, and automatically set the type and size of the treatment device based on the morphological information. Examples of the morphological information include the distance and angle between various feature points, the area, volume, perimeter, surface area of ​​a part or all of the region, circularity, sphericity, etc.

[0102] In this embodiment, the first setting function 243 sets the depth and angle of the treatment device when the treatment device is placed.

[0103] For example, after setting the size of the treatment device as described above, the display control function 248 causes the display 22 to display a drop-down list from which the depth and angle of the treatment device can be selected according to the type and size of the treatment device that has been set.

[0104] The first setting function 243 receives a selection input of the depth and angle of the treatment device via the drop-down list, and sets the received depth and angle of the treatment device as the depth and angle of the treatment device.

[0105] As described above, by setting the depth and angle of the treatment device, for example, when the treatment device to be placed is a clip, it becomes possible to uniquely identify the position of one of the two positions clamped by the treatment device from the position of the other side. Specifically, by setting the clamping position on the anterior leaflet side of the mitral valve, it becomes possible to uniquely identify the clamping position on the posterior leaflet side.

[0106] The first setting function 243 may set a condition related to treatment other than the above as the first treatment condition.

[0107] The second setting function 244 sets a second treatment condition of the treatment method to be estimated. In this embodiment, the second treatment condition is a condition related to the treatment position of the target organ, among the conditions used as conditions for the estimation process executed by the estimation function 245.

[0108] Here, the treatment position refers to the position of a part of the target organ involved in a treatment procedure, such as the position where the structure or properties of the target organ are changed by treatment such as resection, puncture, suturing, or cauterization, the position where a therapeutic drug or other drug is administered or applied, or the position or angle at which a treatment device is placed.

[0109] For example, the second setting function 244 sets the positions (positions on the anterior leaflet side and posterior leaflet side of the mitral valve) at which a treatment device is to be placed in Edge to Edge Repair for mitral regurgitation.

[0110] As an example, when the device to be placed is a clip, first, the second setting function 244 sets the position of the anterior leaflet side to be clamped by the treatment device. Next, the second setting function 244 specifies the position of the posterior leaflet side from the set position of the anterior leaflet side and the depth and angle of the treatment device set as one of the first treatment conditions by the first setting function 243. The second setting function 244 sets the specified position of the posterior leaflet side as the position of the posterior leaflet side to be clamped by the treatment device.

[0111] In the above case, the user can compare the difference in the estimated value of the feature amount after treatment due to the difference in the position where the treatment device is placed under the first treatment condition (type of treatment device, size of treatment device, depth and angle of treatment device) set by the first setting function 243. Since the therapeutic effect of the treatment can be estimated from the estimated value of the feature amount after treatment, it can also be said that the user can compare the difference in the estimated therapeutic effect due to the difference in the position where the treatment device is placed.

[0112] The first setting function 243 may set the position of the posterior cusp side instead of the depth and angle of the treatment device as the first treatment condition. In this case, the user can compare the difference in estimated treatment effect due to the difference in the position of the anterior cusp side at the position of the posterior cusp side set by the first setting function 243.

[0113] The second setting function 244 may also set the position of the posterior apex side, the depth or angle of the treatment device as the second treatment condition. The second setting function 244 may also set a condition related to treatment other than the above as the second treatment condition.

[0114] In short, as long as the first setting function 243 and the second setting function 244 set all conditions related to the treatment used in the estimation process by the estimation function 245, any conditions related to the treatment may be set in each of the first setting function 243 and the second setting function 244.

[0115] For example, the second setting function 244 may set the treatment position by receiving, from the user via a user interface, a designation input of a grid point corresponding to the treatment position.

[0116] Furthermore, for example, the second setting function 244 may set the treatment position using an organ model displayed on an application by the display control function 248. In this case, the second setting function 244 sets the treatment position by receiving a selection input (such as a click) from the user of a corresponding position on the displayed organ model.

[0117] Also, for example, the second setting function 244 may automatically set the treatment position based on the lattice point number or the like without receiving an explicit designation from the user. Also, for example, the second setting function 244 may automatically set the treatment position based on the anatomical features of the organ model.

[0118] Also, for example, when the setting of the treatment position is repeated multiple times (when multiple treatment positions are set), the second setting function 244 may set the treatment position according to the user's specifications only the first time it is set, and from the second time onwards, the treatment position may be automatically set based on pre-set rules.

[0119] The estimation function 245 performs estimation processing to estimate the state of the target organ after treatment based on the first treatment condition and the second treatment condition set by the first setting function 243 and the second setting function 244. For example, the state to be estimated is the form, properties, and dynamics of part or all of the target organ, the state of fluids and gases that are affected by the target organ, and the state of not only the target organ but also the relationship between the target organ and organs surrounding the target organ.

[0120] Here, since the therapeutic effect of the treatment can be estimated from the estimated value of the feature after the treatment, it can also be said that the estimation function 245 estimates the therapeutic effect of the target organ by the treatment corresponding to the first treatment condition and the second treatment condition.

[0121] Any estimation method may be used as long as it can estimate information about the motion of an object or fluid. For example, the estimation function 245 estimates the state of the target organ after treatment using known methods such as the finite element method, the finite difference method, and the immersion boundary method.

[0122] The estimation function 245 may estimate the post-treatment shape of the target organ from a shape model constructed by learning pre-prepared learning data using machine learning techniques including deep learning. For example, in this case, the shape model is a trained model that has learned the relationship between the mitral valve mesh acquired by the second acquisition function 242 and the first and second treatment conditions set by the first setting function 243 and the second setting function 244 as input training data and the post-treatment shape of the target organ as output training data.

[0123] In this embodiment, the estimation function 245 applies a mathematical or physical model that is set based on the first and second treatment conditions set by the first setting function 243 and the second setting function 244 to each grid point of the organ model for the mitral valve acquired by the second acquisition function 242.

[0124] Next, the estimation function 245 estimates the shape of the mitral valve after the treatment device is placed at the treatment position set as the second treatment condition by the second setting function 244. Furthermore, the estimation function 245 calculates the valve orifice area from the estimated mitral valve shape as an estimated value of the feature amount after treatment.

[0125] The estimation function 245 may estimate post-treatment blood flow information using the estimated post-treatment shape of the target organ as a post-treatment feature. The blood flow information may be, for example, the amount of backflow of blood in the target organ. For example, the estimation function 245 may design an electric circuit model that simulates the circulatory dynamics of a living body in advance based on the Windkessel model or the pulse wave propagation model, and input the estimated post-treatment shape of the target organ into the electric circuit model. In this way, the estimation function 245 obtains the estimated post-treatment blood flow information.

[0126] More specifically, the estimation function 245 designs an electric circuit model that can calculate the amount of regurgitated blood flow in the mitral valve based on the valve orifice area of ​​the mitral valve, and inputs the estimated valve orifice area after treatment to the electric circuit model, allowing the estimation function 245 to calculate the estimated amount of regurgitated blood flow in the mitral valve after treatment.

[0127] The method for calculating the blood flow state is not limited to the method using the electric circuit model described above. For example, the estimation function 245 may calculate the blood flow state by numerically obtaining target fluid information by establishing simultaneous equations such as the Navies-Stokes equations and the equation of continuity, Maxwell's equations, or equation of state, and inputting various parameters into the equations.

[0128] Conditions required for the estimation process other than the first and second treatment conditions may be determined in advance, or may be automatically set in accordance with the form of the organ model acquired by the second acquisition function 242. In this case, the conditions include various parameters, boundary conditions, etc. used in the estimation process.

[0129] Specifically, the stiffness, thickness, fiber direction, etc. of the valve may be set as parameters related to the valve leaflets. The stiffness, length, thickness, connection position, number, etc. of the chordae may be set as parameters related to the chordae. The stiffness, volume, surface smoothness, etc. of the blood vessels, left atrium, left ventricle, etc. may be set as parameters related to the cardiac chambers or blood vessels. The viscosity, flow rate, cuff pressure (systolic blood pressure, diastolic blood pressure), total blood volume, etc. of the blood may be set as parameters related to the blood.

[0130] Note that each parameter may not be set directly, but may be set from an indirect index based on a preset algorithm. For example, the flow velocity of blood passing through the mitral valve may be set indirectly from the pressure difference calculated between the upper side (left atrial side) and the lower side (left ventricular side) of the mitral valve based on the amount of change in the volume of the left atrium and left ventricle over time. In other words, the volume of the left atrium and left ventricle may be used as a parameter for setting the flow velocity.

[0131] The above parameters are merely examples, and the present invention is not limited to the type or number of parameters. Any parameters that can be used in the estimation process for estimating the state of a living organism may be set.

[0132] In addition, the estimation function 245 may set parameters related to clinical information recorded in electronic medical records, etc., by connecting to a network within the hospital and identifying the target information from the electronic medical records, HIS, RIS, etc.

[0133] The parameters used by the estimation function 245 for the estimation process may include a mixture of manually set parameters and automatically set parameters. Furthermore, the estimation function 245 may set predetermined constants as initial values ​​of various parameters. The constants may be set based on the attributes of the subject P. For example, the estimation function 245 may set different initial values ​​depending on the age and sex of the subject P.

[0134] Furthermore, the estimation function 245 does not necessarily have to use all predefined parameter items. For example, the estimation function 245 may receive a selection input of parameter items to be used from the user via a user interface.

[0135] Furthermore, for example, the estimation function 245 may calculate the reliability of a parameter automatically acquired by image processing, etc. If the calculated reliability exceeds a threshold, the estimation function 245 may set the calculated value as the parameter, and if the reliability is equal to or less than the threshold, may set a predetermined constant instead of the calculated value as the parameter.

[0136] Also, for example, the estimation function 245 may prompt the user to manually set a specific parameter item, and if the parameter item is not manually set, the estimation function 245 may not use the parameter item.

[0137] Furthermore, for example, when the estimation function 245 acquires parameters from a different system such as an electronic medical record, if the corresponding parameter is not recorded in the system, the estimation function 245 may set a predetermined constant as the parameter. Furthermore, the estimation function 245 may prompt the user to manually set the parameter item. Furthermore, the estimation function 245 may not use parameter items that are not recorded.

[0138] Furthermore, the estimation function 245 may be able to set not only parameters and boundary conditions for the target organ or the target patient, but also calculation parameters for executing the estimation process. For example, the estimation function 245 may be able to set calculation grid setting conditions (e.g., mesh position, number, shape, element type (primary, secondary, etc.) etc.), convergence conditions (number of processing steps (number of loops), time, etc.).

[0139] The determination function 246 determines the display conditions for the treatment position in the organ model based on the estimated state of the target organ.

[0140] For example, the determination function 246 determines the display conditions for the organ model acquired by the second acquisition function 242 on the display 22 based on the estimation result by the estimation function 245. For example, the display conditions are display conditions at a position on the organ model corresponding to the treatment position set by the second setting function 244.

[0141] In this embodiment, the determination function 246 sets the display conditions for the position of the grid point itself corresponding to the treatment position set by the second setting function 244 or the positions around the grid point. Note that the above is just an example, and the position corresponding to the treatment position in the organ model may be specified by any method.

[0142] Furthermore, for example, the display conditions may be conditions related to color (including color and grayscale), such as color, brightness, saturation, and transparency. Furthermore, for example, when the positions of lattice points are shown by figures or symbols such as circles, the display conditions may be conditions related to the shape of the figures or symbols, such as the size, shape, and thickness of the outline of the symbols. Furthermore, for example, when an organ model is shown in a mesh shape in which lattice points are connected by lines, the display conditions may be conditions such as the thickness and color of the lines connecting the lattice points. In short, the display conditions may be any conditions that allow a user to visually recognize differences.

[0143] The determination function 246 may determine in advance the relationship between the estimated values ​​of the feature amounts (valve orifice area, regurgitant amount, etc.) calculated by the estimation function 245 and the display conditions to be set, or may automatically determine the relationship based on the distribution of estimated values ​​estimated at multiple treatment positions. For example, the determination function 246 may determine the display conditions (color, etc.) corresponding to each estimated value based on the maximum and minimum values ​​of the estimated values ​​estimated at multiple positions.

[0144] After the display conditions are determined by the determination function 246, the determination function 247 determines whether or not to perform estimation processing at another treatment position.

[0145] For example, after the display conditions are determined by the decision function 246, the display control function 248 displays on the GUI a message asking the user whether or not to perform estimation processing using the estimation function 245 at another treatment position, as well as a Yes button and a No button.

[0146] When the determination function 247 receives a Yes button press input from the user, it determines that estimation processing will be performed at another treatment position. On the other hand, when the determination function 247 receives a No button press input from the user, it determines that estimation processing will not be performed at another treatment position.

[0147] Furthermore, for example, the determination function 247 may determine whether or not to perform estimation processing by the estimation function 245 at another treatment position based on the number of grid points in the organ model acquired by the second acquisition function 242.

[0148] In this case, the determination function 247 specifies a treatment position for which an estimated value should be calculated in advance based on the number of grid points in the organ model acquired by the second acquisition function 242. The determination function 247 may determine whether or not to perform estimation processing by the estimation function 245 at other treatment positions so that the processing for setting the treatment position, the processing for determining the display conditions, and the estimation processing are repeated until these processing are completed at all grid points corresponding to the treatment position.

[0149] Here, the judgment function 247 may judge all grid points as treatment positions for which an estimated value should be calculated, or may identify grid points that satisfy certain conditions and judge the identified grid points as treatment positions for which an estimated value should be calculated.

[0150] For example, the specific condition may be a condition corresponding to a treatable position under the first treatment condition set by the first setting function 243. Specifically, it is known that the length of the valve leaflet is an application condition for the Edge to Edge Repair treatment device. Therefore, the determination function 247 may determine whether to perform estimation processing at another treatment position so that the processing is repeated until it is completed at a position that satisfies the application condition.

[0151] Furthermore, for example, the determination function 247 may identify the grid points for which an estimated value should be calculated based on the relationship (distance, angle, etc.) with characteristic positions in the organ model, such as the commissures and annulus of the mitral valve, or the relationship with characteristic structures of the target organ, such as the chordae tendineae, or surrounding organs, such as the aortic valve.

[0152] Also, for example, the judgment function 247 may determine whether to perform estimation processing at another treatment position so that a new treatment position is set until the estimated value estimated by the estimation function 245 satisfies a predetermined condition (above a threshold, below a threshold, a specific range, etc.).

[0153] The display control function 248 controls the display of various information on the display device. For example, the display control function 248 displays the organ model acquired by the second acquisition function 242 on the GUI displayed on the display 22 in accordance with the display conditions determined by the determination function 246.

[0154] Next, a description will be given of the processing executed by the medical information processing apparatus 2 according to this embodiment. Fig. 5 is a flowchart showing an example of the processing executed by the medical information processing apparatus 2 according to this embodiment.

[0155] First, the first acquisition function 241 acquires slice image data (step S101). For example, the first acquisition function 241 acquires slice image data from the X-ray CT apparatus 1. In this example, the first acquisition function 241 acquires slice image data depicting the mitral valve in the systolic phase when the mitral valve is closed.

[0156] Next, the second acquisition function 242 acquires a mitral valve mesh MM (organ model) from the slice image data (step S102). For example, the second acquisition function 242 receives a designation input of a region on the slice image from the user, and extracts a mitral valve region from the slice image data in accordance with the designation input. The second acquisition function 242 sets a plurality of lattice points in the mitral valve region, and acquires a mitral valve mesh MM that represents the mitral valve as a three-dimensional mesh model.

[0157] Next, the first setting function 243 performs settings related to the treatment device (setting of first treatment conditions) (step S103). For example, the first setting function 243 selects and sets the treatment device from three types: MitraClip (registered trademark), PASCAL (Edwards Lifesciences), and DragonFly (Hangzhou Valgen Medtech Co. Ltd.) according to the user's input.

[0158] For example, if MitraClip (registered trademark) is determined as the type of treatment device, the first setting function 243 selects and sets the clip width from two types, 4 mm and 6 mm, and the clip length from two types, 9 mm and 12 mm, according to user input. Also, for example, the first setting function 243 sets the depth and angle of the treatment device according to user input.

[0159] Next, the second setting function 244 performs settings related to the treatment position (setting of second treatment conditions) (step S104). For example, the second setting function 244 sets the positions of the anterior leaflet side and the posterior leaflet side of the mitral valve to be clamped by the treatment device of the type and size set in step S103.

[0160] 6 and 7 are diagrams illustrating an example of the treatment position setting process. As shown in Fig. 6, the display control function 248 displays the mitral valve mesh MM acquired in step S102 on the GUI. The second setting function 244 receives, from the user, an input specifying one lattice point from among the lattice points indicating the anterior leaflet region of the mitral valve mesh MM. The second setting function 244 sets the lattice point specified by the user as position AP1 on the anterior leaflet side to be clamped by the treatment device.

[0161] 7, the second setting function 244 identifies a lattice point on the posterior leaflet side corresponding to the set position AP1 from the set position AP1 and the device placement angle DA determined by the depth and angle of the treatment device set in step S103. The second setting function 244 sets the identified lattice point as a position PP1 on the posterior leaflet side sandwiched by the treatment device.

[0162] Returning to Figure 5, the explanation will continue. After step S104, the estimation function 245 estimates the state of the mitral valve after treatment (step S105). For example, the estimation function 245 uses a known method to estimate the shape of the mitral valve after the treatment device is placed at the treatment position set in step S104. Then, the estimation function 245 calculates an estimated valve orifice area after treatment from the estimated mitral valve shape.

[0163] Next, the determination function 246 determines the display conditions of the mitral valve mesh MM (step S106). For example, the determination function 246 sets the display conditions of the lattice points corresponding to the treatment position set in step S104 based on the post-treatment valve orifice area value estimated in step S105.

[0164] 8 is a diagram illustrating an example of the display condition determination process. As shown in FIG. 8, the determination function 246 sets the display conditions for the positions AP1 and PP1 set in step S104 according to the estimated valve orifice area value after treatment estimated in step S105.

[0165] Specifically, the determination function 246 determines whether the estimated valve orifice area value is XX mm 2 ~YYmm 2 If so, it will be displayed in blue, YY+1mm 2 ~ZZmm 2 If so, it will be displayed in yellow, ZZ+1mm 2 ~AAmm 2 The display conditions are determined according to a rule such as, for example, display in red if the color is blue. In the example of Fig. 8, the determination function 246 may change the display conditions between positions AP1 and PP1. For example, the determination function 246 may set the transparency of the display at position PP1 to be lower than the transparency of position AP1.

[0166] Returning to Fig. 5, the description will continue. After step S106, the determination function 247 determines whether to perform estimation processing at another treatment position (step S107). For example, the determination function 247 determines whether to perform estimation processing at another treatment position in accordance with a user instruction.

[0167] If the estimation process is to be performed at another treatment position (step S107: Yes), the process returns to step S103.

[0168] 9 is a diagram illustrating an example of additional setting processing of a treatment position. As shown in FIG. 9, the second setting function 244 sets another position AP2 on the anterior leaflet side to be clamped by the treatment device and another position PP2 on the posterior leaflet side to be clamped by the treatment device in the same procedure as in FIG. 6 and FIG. 7.

[0169] Furthermore, when the second setting function 244 performs processing to set another treatment position, the decision function 246 performs processing to update the display conditions of the organ model.

[0170] 10 is a diagram illustrating an example of the display condition update process. As shown in FIG. 10, the determination function 246 updates the display conditions of the mitral valve mesh MM according to the estimated valve orifice area value after treatment at another treatment position set by the second setting function 244.

[0171] For example, the determination function 246 updates the display conditions of the mitral valve mesh MM by determining the display conditions of positions AP1, PP1, AP2, and PP2 according to the difference (if three or more treatment positions are set, the maximum and minimum estimated valve area values ​​may also be used) between the estimated valve area value (hereinafter also referred to as estimated value EV1) when positions AP1 and PP1 are sandwiched between them and the estimated valve area value (hereinafter also referred to as estimated value EV2) when positions AP2 and PP2 are sandwiched between them.

[0172] As an example, if the display color of the estimated value EV1 and the estimated value EV2 is both blue and the estimated value EV1 is greater than the estimated value EV2, the determination function 246 may update the display conditions by changing the predetermined rule so that the positions AP1 and PP1 are displayed in yellow. This makes it easier for the user to understand the difference in the estimated treatment effect due to the difference in the treatment position.

[0173] Returning to Figure 5, the description continues. If estimation processing is not to be performed at other treatment positions in step S107 (step S107: No), the display control function 248 displays the mitral valve mesh MM acquired in step S102 on the GUI in accordance with the display conditions determined or updated in step S106 (step S108), and ends this processing.

[0174] The determination function 246 may determine the display conditions based on the estimated valve orifice area values ​​at a plurality of treatment positions in step S106 of Fig. 5. In this case, the order of the processes in step S106 and step S107 is reversed.

[0175] After step S106, the process of step S108 may be executed without proceeding to the process of step S107.

[0176] Here, it can be said that the positions of the anterior leaflet and posterior leaflet of the mitral valve sandwiched by the treatment device set in step S104 represent the treatment position, and the valve orifice area value after treatment estimated in step S105 represents the treatment effect. Therefore, in step S108 of the above example, it can be said that the display control function 248 displays, on the organ model of the target organ, information representing the relationship between the treatment position designated by the user and the treatment effect estimated when treatment is performed at that treatment position.

[0177] In the above example, if the user additionally specifies another treatment position after step S108, the display control function 248 may reset information representing the relationship between the currently specified treatment position on the organ model of the target organ and the predicted therapeutic effect when treatment is performed at the currently specified treatment position. In this case, the display control function 248 may display information representing the relationship between the other treatment position and the predicted therapeutic effect when treatment is performed at the other treatment position after the reset.

[0178] In addition, the display control function 248 may display an organ model representing information showing the relationship between the currently specified treatment position and the treatment effect at the currently specified treatment position, and an organ model representing information showing the relationship between another treatment position and the treatment effect at the other treatment position, side by side.

[0179] The medical information processing device 2 according to the embodiment described above acquires an organ model representing the organ to be treated, identifies treatment conditions including the treatment position in the organ model, estimates post-treatment features based on the organ model and the identified treatment conditions, determines display conditions for the treatment position in the organ model based on the estimated features, and displays the organ model on the display 22 in accordance with the determined display conditions.

[0180] As a result, the medical information processing device 2 according to the embodiment can change the display form at a position corresponding to a treatment position on an organ model representing a pre-treatment state, depending on the feature amount estimated from the treatment conditions including the treatment position. Therefore, a user can confirm the relationship between a treatment position and the estimated value of the feature amount after treatment at the treatment position on the organ model representing the pre-treatment state. Since the treatment effect can be estimated from the estimated value of the feature amount after treatment, the medical information processing device 2 according to the embodiment can easily allow a user to grasp the relationship between a pre-treatment state, treatment conditions, and the treatment effect estimated under the treatment conditions.

[0181] The above-described embodiment can be modified as needed by partially changing the configuration or functions of each device. Therefore, several modifications of the above-described embodiment will be described below as other embodiments. The following mainly focuses on differences from the above-described embodiment, and detailed descriptions of commonalities with the content already described will be omitted. The modifications described below may be implemented individually or in appropriate combination.

[0182] (Variation 1) In the above-described embodiment, the medical image is described as a CT image obtained from the X-ray CT device 1, but the present invention is not limited to this. The medical image may be an image obtained from a medical imaging diagnostic device such as an MRI (Magnetic Resonance Imaging) device, an angio-CT system, a tomosynthesis device, a SPECT (Single Photon Emission Computed Tomography) device, a PET (Positron Emission Computed Tomography) device, or an ultrasound diagnostic device.

[0183] The type of medical image may be any type of image that stores morphological information of the target organ, such as a three-dimensional or two-dimensional medical image, or a four-dimensional image obtained by capturing multiple images of these over time.

[0184] According to this modification, it is possible to easily grasp the relationship between a treatment condition and a treatment effect estimated under that treatment condition, based on a medical image other than a CT image.

[0185] (Variation 2) In the above-described embodiment, the treatment method is treatment of the mitral valve using a treatment device for Edge to Edge Repair. However, the target organ and the treatment method (for example, treatment device or procedure) are not limited to this.

[0186] For example, this method can be applied to transcatheter valve replacement (TAVI) for aortic regurgitation. An example of applying this method to transcatheter valve replacement for aortic regurgitation will be described below with reference to Figure 5.

[0187] 5, the first acquisition function 241 acquires a medical image depicting the aortic valve, the ascending aorta, and the left ventricular outflow tract (LVOT). Also, for example, in step S102, the second acquisition function 242 acquires an organ model representing the aortic valve, the ascending aorta, and the left ventricular outflow tract (LVOT).

[0188] Also, for example, in step S103, the first setting function 243 sets the size and type of the treatment device. Also, for example, in step S104, the second setting function 244 sets the placement position (for example, depth) of the treatment device.

[0189] The second setting function 244 may set the placement angle of the treatment device instead of the depth of the treatment device. The second setting function 244 may also set the linear distance from the coronary artery entrance to the tip of the device as a condition for the treatment position.

[0190] Also, for example, in step S105, the estimation function 245 estimates the blood flow rate at the aortic valve and the blood flow rate to the coronary arteries. Also, for example, in step S106, the determination function 246 determines the display conditions according to the estimated blood flow rate to the coronary arteries.

[0191] 11 to 13 are diagrams illustrating an example of setting display conditions for an organ model according to Modification 2. Fig. 11 shows an example of a treatment position TP set on a three-dimensional mesh model AA (hereinafter also referred to as an aortic valve mesh AA) including an area near the aortic valve. In this case, the treatment position TP represents the position of the tip of a treatment device (artificial valve).

[0192] In this example, the estimation function 245 estimates the amount of blood flow into the coronary artery when the artificial valve is placed so that its tip position is at the set treatment position TP as a post-treatment feature. The determination function 246 determines the display condition DC5 at the treatment position according to the estimated amount of blood flow into the coronary artery, as shown in Fig. 12. Here, because the tip of the artificial valve is circular, the display conditions for all of the lattice points around the circumference corresponding to the tip position of the artificial valve in the aortic valve mesh AA are the same.

[0193] Furthermore, by setting the tip positions of multiple artificial valves and performing the same processing as above, the user can visually understand the difference in the estimated value of blood flow to the coronary artery due to the difference in the position where the artificial valve is placed, as shown by display conditions DC5 to DC8 in Figure 13. The user can also estimate the treatment effect from the blood flow to the coronary artery. Therefore, when this method is applied to transcatheter valve replacement surgery for aortic regurgitation, the user can easily understand the difference in the estimated treatment effect due to the difference in the position where the artificial valve is placed.

[0194] Furthermore, for example, this technique can be applied to cerebral aneurysm clipping surgery for cerebral aneurysms. Cerebral aneurysm clipping surgery is a procedure to prevent rupture (subarachnoid hemorrhage) of unruptured cerebral aneurysms. Below, an example of applying this technique to cerebral aneurysm clipping surgery for cerebral aneurysms will be described using Figure 5.

[0195] 5, the first acquisition function 241 acquires a medical image depicting the cerebral arteries of the subject P, including the portion where the cerebral aneurysm is present. Also, for example, in step S102, an organ model representing the cerebral arteries is acquired.

[0196] Also, for example, in step S103, the first setting function 243 sets the size and type of the treatment device (clip). Also, for example, in step S104, the second setting function 244 sets the placement position and placement angle of the clip.

[0197] Also, for example, in step S105, the estimation function 245 estimates the amount of blood flow to the cerebral aneurysm. Also, for example, in step S106, the determination function 246 determines the display conditions according to the estimated amount of blood flow to the cerebral aneurysm.

[0198] In the above example, the user can visually understand the difference in estimated blood flow to the cerebral aneurysm due to differences in the clip placement position and placement angle. The user can also estimate the treatment effect from the blood flow to the cerebral aneurysm. Therefore, when this method is applied to aneurysm clipping surgery for cerebral aneurysms, the user can easily understand the difference in estimated treatment effect due to differences in the clip placement position and placement angle.

[0199] (Variation 3) In the above embodiment, a configuration in which the second setting function 244 sets a treatment condition related to one treatment position has been described. In this modified example, a configuration in which another condition can be set in addition to the condition related to the treatment position will be described. Hereinafter, an example in which the second setting function 244 sets two types of treatment conditions, namely, the treatment position and the device size, will be described with reference to FIG. 5.

[0200] 5, the second setting function 244 can set two types of treatment conditions: treatment position and device size. Also, for example, in step S105, the estimation function 245 estimates the post-treatment state based on the two types of set conditions. Also, for example, in this modification, the processing of step S107 is performed before step S106.

[0201] In this case, in step S107, the determination function 247 determines whether to estimate post-treatment conditions for other device sizes at the same position. When the second setting function 244 repeats step S103 to set multiple device sizes as treatment conditions, the estimation function 245 can estimate post-treatment conditions for multiple device sizes at the same position by repeating step S105.

[0202] Also, for example, in step S106, the decision function 246 calculates the difference in results due to differences in device size at the same treatment position. In this case, the decision function 246 changes the display conditions according to the magnitude of the difference in the calculated results.

[0203] For example, the determination function 246 sets the transparency to low when the difference is small, and sets the transparency to high when the difference is large, which makes it easier for the user to identify positions where the difference in device size has a large or small effect.

[0204] Although the above describes an example in which device size is set as a treatment condition in addition to treatment position, the treatment condition set by the second setting function 244 along with the treatment position is not limited to device size. Furthermore, the number of types of treatment conditions is not limited to two. For example, the second setting function 244 may be configured to set three or more types of treatment conditions.

[0205] However, in the above case, the determination function 246 needs to determine different display conditions for a plurality of treatment conditions so that the differences in the display conditions are visible. For example, the determination function 246 may express the differences in treatment conditions using brightness, saturation, frame thickness, frame color, shape, etc. in addition to transparency. In this modification, the second setting function 244 needs to set a treatment condition related to the position of at least one target organ.

[0206] According to this modification, for example, the user can visually understand the differences between a plurality of types of treatment conditions at the same treatment position for each treatment condition.

[0207] (Variation 4) In the above-described embodiment, the determination function 246 determines the display conditions according to the estimated values ​​of the feature amounts after treatment. In this modified example, the determination function 246 corrects the display conditions using information other than the estimated values ​​estimated by the estimation function 245.

[0208] Here, the information other than the estimated value is, for example, information on the position of the target organ, information on the relationship with surrounding organs, information on the attributes of the user or patient, etc. Furthermore, correcting the display conditions means, for example, setting the display conditions based on both the estimated values ​​of the feature amounts estimated by the estimation function 245 and information other than the estimated values, or changing or modifying the display conditions set based on the estimated values ​​of the feature amounts based on information other than the estimated values.

[0209] As an example, the determination function 246 manually or automatically evaluates the ease of placement of a therapeutic device in a target organ. The determination function 246 corrects the display conditions using the evaluation result. More specifically, if the user evaluates that it is easier to place a therapeutic device near the center of the mitral valve leaflets than near the commissures of the mitral valve, the determination function 246 may determine the display conditions so that the transparency near the center of the mitral valve leaflets is lower than near the commissures of the mitral valve.

[0210] This allows the user to view the target organ model under display conditions that take into account both the ease of placement of the treatment device and the estimated values ​​of the feature quantities after treatment, making it easier for the user to determine the position where the treatment device should be placed.

[0211] Furthermore, for example, the determination function 246 may identify the position of the chordae tendineae using a known method and correct the display conditions according to the distance from the identified position of the chordae tendineae. Furthermore, for example, the determination function 246 may set in advance placement positions that are favorable or unfavorable for the user and correct the display conditions based on those positions. Furthermore, for example, the determination function 246 may correct the display conditions according to the patient's age, medication history, treatment history, etc.

[0212] Furthermore, for example, the determination function 246 may correct the display conditions based on estimated values ​​of feature amounts after treatment at a plurality of treatment positions. As an example, the determination function 246 may correct the display conditions when a predetermined condition is satisfied, such as when estimated values ​​of feature amounts similar to each other are estimated at adjacent grid points when the adjacent grid points are set as treatment positions.

[0213] In the above description, the case where similar feature values ​​are estimated is described as the predetermined condition, but the predetermined condition may be the case where high feature values ​​are estimated, or low feature values ​​are estimated, etc. Furthermore, when there are consecutive lattice points that satisfy the predetermined condition, the determination function 246 may correct the display condition according to the number of consecutive lattice points.

[0214] (Variation 5) In the above embodiment, the estimation function 245 estimates one feature amount. In this modification, the estimation function 245 estimates multiple feature amounts. For example, the estimation function 245 may estimate post-treatment feature amounts for two feature amounts, the valve orifice area and the regurgitation amount.

[0215] When estimating the estimated values ​​of multiple feature quantities, for example, the determination function 246 may add a process of integrating the estimated values ​​of each feature quantity to calculate a single estimated value, and set the display conditions based on the integrated estimated value.

[0216] Furthermore, for example, the determination function 246 may set different display conditions for each of the estimated values ​​of the plurality of feature quantities. As an example, the determination function 246 may represent the estimated value of the valve orifice area by color and the estimated value of the regurgitant amount by transparency.

[0217] According to this modification, treatment conditions can be considered taking into account multiple feature quantities. Furthermore, according to this modification, different display conditions can be associated with the estimated values ​​of multiple feature quantities, so that the user can easily visually recognize the differences in the estimated values ​​of multiple feature quantities due to differences in treatment positions.

[0218] (Variation 6) In the above-described embodiment, the display control function 248 does not control the display 22 to display the treatment conditions related to the treatment device set by the first setting function 243 in step S103 of Fig. 5. In this modified example, the display control function 248 controls the display 22 to display the treatment conditions related to the treatment device set by the first setting function 243.

[0219] For example, when an organ model is displayed on the display 22 in step S108 of FIG. 5, the display control function 248 may perform control to display the treatment conditions set by the first setting function 243 near the organ model.

[0220] The conditions related to the treatment device set by the first setting function 243 in step S103 of Fig. 5 are not expressed as display conditions of the target organ model. Therefore, by displaying the treatment conditions related to the treatment device as characters or numbers nearby, it is possible to further promote user understanding.

[0221] (Variation 7) In the above-described embodiment, the medical information processing device 2 has the first acquisition function 241, the second acquisition function 242, the first setting function 243, the second setting function 244, the estimation function 245, the determination function 246, the determination function 247, and the display control function 248. However, all or part of these functions may be provided by other devices.

[0222] For example, the console device 40 of the X-ray CT device 1 may have a first acquisition function 241, a second acquisition function 242, a first setting function 243, a second setting function 244, an estimation function 245, a decision function 246, a judgment function 247, and a display control function 248.

[0223] Furthermore, for example, the medical information processing system S may further include a device such as a workstation separate from the medical information processing device 2, and the workstation may have some of the functions of the first acquisition function 241, the second acquisition function 242, the first setting function 243, the second setting function 244, the estimation function 245, the decision function 246, the judgment function 247, and the display control function 248.

[0224] As a result, the medical information processing system S according to this modification can concentrate the above functions in one device or distribute the above functions among multiple devices. Therefore, the medical information processing system S according to this modification can adopt a system configuration that suits the user's environment.

[0225] According to at least the embodiment and modified examples described above, it is possible to easily grasp the relationship between the state before treatment, the treatment conditions, and the treatment effect estimated under the treatment conditions.

[0226] The term "processor" used in the above description refers to circuits such as a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)).

[0227] The processor realizes its functions by reading and executing the programs stored in the memory 41. Note that instead of storing the programs in the memory 41, the programs may be directly embedded in the circuitry of the processor. In this case, the processor realizes its functions by reading and executing the programs embedded in the circuitry.

[0228] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0229] 1 X-ray CT device 2 Medical information processing equipment 10 Mounting device 11 X-ray tube 12 X-ray detector 13 Rotating Frame 14 X-ray high voltage device 15 Control device 16 Wedge 17 Collimator 18 DAS(Data Acquisition System) 19 Photography 21 Memory 22 Display 23 Input Interface 24 Processing circuit 241 First Acquisition Function 242 Second Acquisition Function 243 First Setting Function 244 Second Setting Function 245 Estimation Function 246 Decision Function 247 Judgment Function 248 Display Control Function 30 Bed Device 31 Foundation 32 Bed drive unit 33 Top plate 34 Support frame 40 Console device 41 memory 42 Display 43 Input Interface 45 Processing circuit 451 System Control Functions 452 Pre-processing function 453 Reconstruction Processing Function 454 Image Processing Function

Claims

1. an acquisition unit that acquires an organ model representing an organ to be treated based on a medical image; an identification unit that identifies a treatment condition indicating a condition related to treatment including at least a treatment position in the organ model; an estimation unit that estimates post-treatment feature amounts based on the organ model and the treatment conditions; a determination unit that determines a display condition for the treatment position in the organ model based on the estimated feature amount; a display control unit that displays the organ model on a display device according to the determined display conditions; A medical information processing device comprising:

2. the specifying unit specifies the treatment condition including a plurality of the treatment positions; the estimation unit estimates the feature amount at each of the plurality of treatment positions; the determination unit determines a display condition for the treatment position in the organ model based on the estimated plurality of feature amounts. The medical information processing device according to claim 1 .

3. the specifying unit receives an input from a user specifying a region on the organ model, and specifies the treatment position based on the input. The medical information processing device according to claim 1 .

4. the treatment conditions include device conditions related to a treatment device for treating the organ; the specifying unit receives an input specifying the device condition from a user, specifies the device condition based on the input, and specifies the treatment position based on the input and the specified device condition; The medical information processing device according to claim 3 .

5. Evaluating the ease of placement of the treatment device and correcting the determined display conditions based on the evaluation results. The medical information processing device according to claim 4 .

6. the display control unit causes the display device to display information representing the identified device condition together with the organ model. The medical information processing device according to claim 4 .

7. the estimation unit estimates a plurality of types of feature amounts; a calculation unit that calculates an integrated feature amount by integrating the estimated multiple types of feature amounts into one; the determination unit determines the display condition based on the calculated integrated feature. The medical information processing device according to claim 1 .

8. The display conditions include at least one of conditions regarding color, brightness, saturation, transparency, thickness of a frame representing the treatment position, color of the frame, and shape of the frame. The medical information processing device according to any one of claims 1 to 7.

9. the estimation unit estimates a plurality of types of feature amounts; the determination unit determines different types of the display conditions for each type of the feature amount. The medical information processing device according to claim 8 .

10. Further, a reception unit is provided for receiving input of information representing the treatment position from a user, The identification unit identifies the treatment position based on the received information representing the treatment position, the estimation unit estimates the post-treatment feature amount when treatment is performed on the identified treatment position as a treatment effect when treatment is performed on the treatment position; the determination unit determines the display conditions based on the estimated degree of therapeutic effect; the display control unit displays information indicating a relationship between the treatment position and the treatment effect on the organ model. The medical information processing device according to claim 1 .

11. the receiving unit receives input of information representing the plurality of treatment positions; the determination unit determines the display conditions so that the treatment effects at each of the plurality of treatment positions can be distinguished. The medical information processing device according to claim 10.

12. the acquisition unit acquires the organ model representing a mitral valve; the specifying unit specifies the treatment conditions including at least the type and size of a mitral valve repair device used in an Edge to Edge Repair that increases a coaptation area by grasping the anterior leaflet and the posterior leaflet of the mitral valve, and the positions of the anterior leaflet and the posterior leaflet grasped by the mitral valve repair device, which is the treatment position; the estimation unit estimates the feature amount related to a shape of the mitral valve after the anterior leaflet and the posterior leaflet are grasped by the mitral valve repair device. The medical information processing device according to any one of claims 1 to 7.

13. A medical information processing method by a medical information processing device, comprising: an acquisition step of acquiring an organ model representing an organ to be treated based on a medical image; a step of identifying treatment conditions indicating conditions related to treatment including at least a treatment position in the organ model; an estimation step of estimating post-treatment feature amounts based on the organ model and the treatment conditions; a determining step of determining a display condition for the treatment position in the organ model based on the estimated feature amount; a display control step of displaying the organ model on a display device according to the determined display conditions; A medical information processing method including:

14. The computer of the medical information processing device an acquisition step of acquiring an organ model representing an organ to be treated based on a medical image; a step of identifying treatment conditions indicating conditions related to treatment including at least a treatment position in the organ model; an estimation step of estimating post-treatment feature amounts based on the organ model and the treatment conditions; a determining step of determining a display condition for the treatment position in the organ model based on the estimated feature amount; a display control step of displaying the organ model on a display device according to the determined display conditions; A program that executes the following.

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