Medical information processing device, medical information processing system, and medical information processing method

Abstract

To facilitate utilization of an index value concerning the blood stream.SOLUTION: Medical information processing equipment according to an embodiment comprises a storage part, a setting part, and a calculation part. The storage part stores a plurality of settings concerning at least any one of a calculation condition, a shape, a property and a fluid. The setting part selects at least any one of the plurality of settings, and determines it as an analysis condition. The calculation part calculates the degree of reliability in regard to an index value concerning the blood stream, which is calculated under the analysis condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in this specification and the drawings relate to a medical information processing apparatus, a medical information processing system, and a medical information processing method. [Background technology]

[0002] Conventionally, a technique for calculating blood flow index values ​​and presenting them to users such as doctors has been known as a technique for assisting in the diagnosis of heart and vascular diseases, formulation of treatment plans, etc. Examples of such index values ​​include wall shear stress (WSS) in blood vessels and fractional flow reserve (FFR). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] U.S. Patent No. 6,654,628 [Patent Document 2] U.S. Patent No. 10,658,085 [Patent Document 3] US Patent Application Publication No. 2019 / 0082970 Summary of the Invention [Problem to be solved by the invention]

[0004] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to facilitate the use of index values ​​related to blood flow. 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 the configurations shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0005] The medical image processing device of the embodiment includes a storage unit, a setting unit, and a calculation unit. The storage unit stores a plurality of settings for at least one of calculation conditions, shape, properties, and fluid. The setting unit selects at least one of the plurality of settings and sets it as an analysis condition. The calculation unit calculates the reliability of an index value related to blood flow calculated under the analysis condition. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a medical information processing system and a medical information processing apparatus according to the first embodiment. [Figure 2] FIG. 2 is a flowchart showing the processing procedure of the processing performed by each processing function of the processing circuitry of the medical image processing apparatus according to the first embodiment. [Figure 3A] FIG. 3A is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 3B] FIG. 3B is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 11A] FIG. 11A is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 11B] FIG. 11B is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 12] FIG. 12 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 13] FIG. 13 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 14] FIG. 14 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 15] FIG. 15 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 16] FIG. 16 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 17] FIG. 17 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 18] FIG. 18 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 19] FIG. 19 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 20] FIG. 20 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 21] FIG. 21 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 22] FIG. 22 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 23] FIG. 23 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 24] FIG. 24 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 25] FIG. 25 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 26] FIG. 26 is a diagram illustrating an example of a reliability calculation method according to the first embodiment. [Figure 27]FIG. 27 is a flowchart showing the processing procedure of the processing performed by each processing function of the processing circuitry of the medical image processing apparatus according to the second embodiment. [Figure 28] FIG. 28 is a flowchart showing the processing procedure of the processing performed by each processing function of the processing circuitry of the medical image processing apparatus according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, embodiments of a medical information processing apparatus, a medical information processing system, and a medical information processing method will be described in detail with reference to the accompanying drawings.

[0008] (First embodiment) FIG. 1 is a diagram showing an example of the configuration of a medical information processing system and a medical information processing apparatus according to the first embodiment.

[0009] 1, a medical information processing system 100 according to this embodiment includes an X-ray CT (Computed Tomography) device 110, a medical image storage device 120, departmental systems 130, a medical information display device 140, and a medical information processing device 150. Here, each device and system is communicably connected via a network NW.

[0010] The X-ray CT device 110 generates a CT image of a subject. Specifically, the X-ray CT device 110 collects projection data representing the distribution of X-rays that have passed through the subject by rotating an X-ray tube and an X-ray detector on a circular orbit that surrounds the subject. The X-ray CT device 110 then generates a CT image based on the collected projection data.

[0011] 1 illustrates an example in which the medical information processing system 100 includes the X-ray CT device 110, but the medical information processing system 100 may include a different type of modality than the X-ray CT device 110. For example, instead of or in addition to the X-ray CT device 110, the medical information processing system 100 may include a medical image diagnostic device such as a magnetic resonance imaging (MRI) device, an ultrasound diagnostic device, a PET (Positron Emission Tomography) device, or a SPECT (Single Photon Emission Computed Tomography) device.

[0012] The medical image storage device 120 stores various medical images related to subjects. Specifically, the medical image storage device 120 acquires CT images from the X-ray CT device 110 via the network NW, and stores the CT images in a memory circuit within the device. For example, the medical image storage device 120 is realized by a computer device such as a server or a workstation. Furthermore, for example, the medical image storage device 120 is realized by a PACS (Picture Archiving and Communication System) or the like, and stores the CT images in a format compliant with DICOM (Digital Imaging and Communications in Medicine).

[0013] Each department system 130 includes various systems such as a Hospital Information System (HIS), a Radiology Information System (RIS), a diagnostic report system, a Laboratory Information System (LIS), a rehabilitation department system, a dialysis department system, and a surgery department system. The medical information processing system 100 is connected to each of these systems and transmits and receives various information to and from each of them. For example, the medical information processing system 100 transmits and receives patient information, examination information, treatment information, information related to analysis results, and the like to and from each of the systems included in each department system 130.

[0014] The medical information display device 140 displays various types of medical information related to the subject. Specifically, the medical information display device 140 acquires medical information such as CT images collected from the subject by the X-ray CT device 110 and results of analysis processing in the medical information processing device 150 via the network NW, and displays the information on a display within the device. For example, the medical information display device 140 is realized by a computer device such as a workstation, a personal computer, or a tablet terminal.

[0015] The medical information processing device 150 is an analysis device that performs analysis processing and calculates index values ​​related to the blood flow of a subject. For example, the medical information processing device 150 acquires CT images from the X-ray CT device 110 or the medical image storage device 120 via the network NW. The medical information processing device 150 also acquires various information such as blood test results and medical records from each department system 130 via the network NW. The medical information processing device 150 then performs analysis processing based on the various medical information acquired via the network NW and calculates index values ​​such as WSS and FFR. Furthermore, the medical information processing device 150 calculates the reliability of the index values. For example, the medical information processing device 150 is realized by computer equipment such as a server or a workstation.

[0016] For example, the medical information processing device 150 includes a network (NW) interface 151, a memory 152, an input interface 153, a display 154, and a processing circuit 155.

[0017] The NW interface 151 controls the transmission and communication of various data transmitted and received between the medical information processing device 150 and other devices connected via the network NW. Specifically, the NW interface 151 is connected to the processing circuitry 155, and outputs data received from other devices to the processing circuitry 155, or transmits data output from the processing circuitry 155 to other devices. For example, the NW interface 151 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

[0018] The memory 152 stores various data and programs. Specifically, the memory 152 is connected to the processing circuitry 155, and stores data input from the processing circuitry 155, or reads out stored data and outputs it to the processing circuitry 155. For example, the memory 152 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

[0019] For example, the memory 152 stores settings for calculation conditions, shape, properties, and fluid for the analysis processing executed by the processing circuitry 155. The memory 152 also stores various types of medical information acquired from the X-ray CT device 110, the medical image storage device 120, each department system 130, etc. via the network NW. The memory 152 also stores programs that enable circuits included in the medical information processing device 150 to realize their functions. The memory 152 is an example of a storage unit.

[0020] The input interface 153 accepts various input operations from the user, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuit 155. For example, the input interface 153 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. The input interface 153 may also be configured as a tablet terminal or the like that can wirelessly communicate with the medical information processing device 150. The input interface 153 may also be a circuit that accepts input operations from the user using motion capture. For example, the input interface 153 can accept the user's body movements, line of sight, and the like as input operations by processing signals acquired via a tracker and images collected about the user. The input interface 153 is not limited to those that include physical operating components such as a mouse and keyboard. For example, an example of the input interface 153 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 150 and outputs this electrical signal to the processing circuit 155.

[0021] The display 154 displays various types of information. For example, the display 154 displays a GUI (Graphical User Interface) for receiving instructions from a user via the input interface 153. The display 154 is realized by a liquid crystal display, a CRT (Cathode Ray Tube) display, a touch panel, or the like.

[0022] 1, the medical information processing device 150 is described as including the display 154, but the medical information processing device 150 may include a projector instead of or in addition to the display 154. The projector can project onto a screen, a wall, a floor, the body surface of a subject, etc. under the control of the processing circuitry 155. As an example, the projector can also project onto any plane, object, space, etc. by projection mapping.

[0023] The processing circuitry 155 controls the entire medical information processing device 150. For example, the processing circuitry 155 performs various processes in response to input operations received from a user via the input interface 153. For example, the processing circuitry 155 inputs data transmitted from another device from the NW interface 151 and stores the input data in the memory 152. Furthermore, for example, the processing circuitry 155 transmits the data input from the memory 152 to another device by outputting the data to the NW interface 151. Furthermore, for example, the processing circuitry 155 displays the data input from the memory 152 on the display 154.

[0024] The above describes exemplary configurations of the medical information processing system 100 and medical information processing device 150 according to this embodiment. For example, the medical information processing system 100 and medical information processing device 150 according to this embodiment are installed in medical facilities such as hospitals and clinics, and support users such as doctors in diagnosing heart and vascular diseases, formulating treatment plans, and the like.

[0025] Specifically, the processing circuitry 155 of the medical information processing device 150 performs analysis processing based on various medical information acquired from the X-ray CT device 110, the medical image storage device 120, the department systems 130, etc., calculates index values ​​related to blood flow, such as WSS and FFR, and provides them to the user. For example, the processing circuitry 155 transmits the calculated index values ​​to the medical information display device 140, which then displays the index values. Alternatively, the processing circuitry 155 can display the index values ​​on the display 154. The user can refer to the index values ​​related to the subject's blood flow as reference information for diagnosis, formulation of a treatment plan, etc.

[0026] Furthermore, the processing circuitry 155 calculates the reliability of the index value related to blood flow, thereby making the index value easier to use. The functions of the processing circuitry 155 will be described below. For example, as shown in FIG. 1, the processing circuitry 155 executes a setting function 155a, a calculation function 155b, and an output function 155c. The setting function 155a is an example of a setting unit. The calculation function 155b is an example of a calculation unit. The output function 155c is an example of an output unit.

[0027] The setting function 155a determines the analysis conditions. The calculation function 155b executes an analysis process under the analysis conditions determined by the setting function 155a to calculate an index value related to blood flow. The calculation function 155b also calculates the reliability of the index value calculated under the analysis conditions determined by the setting function 155a. The output function 155c outputs the index value and reliability calculated by the calculation function 155b. For example, the output function 155c controls the NW interface 151 to transmit the index value and reliability to the medical information display device 140, and causes the display 154 to display the index value and reliability.

[0028] The above-described processing circuitry 155 is realized by, for example, a processor. In this case, each of the above-described processing functions is stored in memory 152 in the form of a program executable by a computer. Then, processing circuitry 155 realizes the function corresponding to each program by reading and executing each program stored in memory 152. In other words, when each program is read, processing circuitry 155 has each of the processing functions shown in FIG. 1.

[0029] The processing circuitry 155 may be configured by combining multiple independent processors, and each processor may execute a program to realize each processing function. Furthermore, each processing function of the processing circuitry 155 may be realized by being appropriately distributed or integrated among a single or multiple processing circuits. Furthermore, each processing function of the processing circuitry 155 may be realized by a combination of hardware and software, such as circuits. While the example described here is one in which programs corresponding to each processing function are stored in a single memory 152, the embodiment is not limited to this. For example, the programs corresponding to each processing function may be stored in a distributed manner among multiple storage circuits, and the processing circuitry 155 may read and execute each program from each storage circuit.

[0030] Next, an overview of a series of processes performed in the medical information processing device 150 will be explained using Fig. 2. Fig. 2 is a flowchart showing the processing procedure of processes performed by each processing function of the processing circuitry 155 of the medical information processing device 150 according to the first embodiment. In Fig. 2, WSS will be explained as an index value related to blood flow.

[0031] First, the calculation function 155b acquires various data to be used for analysis (step S101). For example, the calculation function 155b acquires a coronary artery CT image of the subject from the X-ray CT device 110 or the medical image storage device 120 via the NW interface 151.

[0032] Next, the setting function 155a sets the analysis conditions (step S102). For example, the analysis conditions for calculating the WSS are settings for the calculation conditions, shape, properties, and fluid. The calculation conditions are, for example, the mesh size and the calculation model. The shape is, for example, the shape of the blood vessel outline or core line, or a method for acquiring the same. The properties are, for example, information such as the composition and hardness of the heart, blood vessels and their surrounding tissues, plaque in the blood vessels, etc., or a method for acquiring the same. The fluid is, for example, fluid information such as the blood flow rate and pressure, or a method for acquiring the same. The settings for the calculation conditions, shape, properties, and fluid are stored in the memory 152. The setting function 155a can determine the analysis conditions by reading the settings from the memory 152.

[0033] Here, the memory 152 may store only one setting for each of the calculation conditions, shape, properties, and fluid, or may store multiple settings. For example, if there is only one calculation model that can be executed by the calculation function 155b, the memory 152 stores that calculation model as the setting for the calculation conditions. Furthermore, the setting function 155a sets the calculation model read out from the memory 152 as the analysis condition.

[0034] On the other hand, if there are multiple calculation models that the calculation function 155b can execute, the memory 152 stores the multiple calculation models as settings for the calculation conditions. Also, the setting function 155a selects one of the multiple calculation models stored in the memory 152 and sets it as the analysis condition. That is, if the memory 152 stores multiple settings for at least one of the calculation conditions, shape, properties, and fluid, the setting function 155a selects one of the multiple settings and sets it as the analysis condition.

[0035] Next, the calculation function 155b calculates the WSS and reliability under the analysis conditions set by the setting function 155a (step S103). That is, the calculation function 155b performs analysis according to the analysis conditions and calculates the WSS. For example, the calculation function 155b calculates the WSS value in the target region from a coronary artery CT image of the subject using a known method such as a finite element method based on CFD (Computational Fluid Dynamics), machine learning, etc. For example, when calculating the index value using CFD, the calculation function 155b performs fluid analysis using conditions such as blood physical properties (e.g., hematocrit, blood viscosity, density, etc.), vascular wall elasticity, iterative calculation conditions (e.g., maximum number of iterations in iterative calculation, relaxation coefficient, tolerance for residuals, etc.), and analysis initial values ​​(e.g., blood flow rate, pressure, fluid resistance, initial value of pressure boundary, etc.), as well as vascular shape data, to calculate the WSS at each position of the coronary artery. The calculation function 155b also evaluates the analysis conditions to calculate the reliability of the WSS calculated under the analysis conditions. The calculation of the reliability will be described in detail later.

[0036] Then, the output function 155c displays the WSS and reliability calculated by the calculation function 155b on the display 154 (step S104). For example, the output function 155c generates a three-dimensional image of the coronary artery by three-dimensionally reconstructing the vascular region of the coronary artery in the coronary artery CT image. As an example, the output function 155c generates a volume rendering (VR) image, a surface rendering (SR) image, a curved planar reconstruction (CPR) image, a multi-planar reconstruction (MPR) image, a stretched multi-planar reconstruction (SPR) image, etc. The output function 155c also generates a display image in which a WSS value is assigned to each position in the three-dimensional image. For example, the output function 155c generates a color image in which a color corresponding to the WSS value is assigned to each position in the three-dimensional image, and displays the color image on the display 154.

[0037] Furthermore, the output function 155c displays the calculated reliability. For example, the output function 155c displays a numerical value indicating the reliability on the display 154. Also, for example, the output function 155c generates a display image in which a reliability value is assigned to each position of the three-dimensional image, and displays the image on the display 154.

[0038] As shown in Figure 2, when the WSS and its reliability are displayed, the user can refer to the WSS when formulating a diagnosis or treatment plan. Furthermore, the reliability can be used to determine the reliability of the WSS, making it easier to use the WSS. For example, if the WSS value suggests the presence of a disease and the reliability is high, the user can more clearly understand the need for treatment for that disease.

[0039] Next, a method for calculating reliability will be described in more detail with reference to Figures 3A to 26. Figures 3A to 26 are diagrams showing an example of a method for calculating reliability according to the first embodiment.

[0040] For example, as shown in Fig. 3A, the calculation function 155b calculates reliability A1 related to mesh size, reliability A2 related to mesh shape, reliability A3 related to mesh quality, reliability A4 related to time resolution, and reliability A5 related to the calculation model, and calculates the reliability of the calculation conditions based on the reliability A1 to A5. Also, as shown in Fig. 3B, the calculation function 155b calculates reliability B1 related to image type, reliability B2 related to imaging conditions, reliability B3 related to blood vessel type, reliability B4 related to blood vessel shape, reliability B5 related to intravascular structures, reliability B6 related to intermediate calculation results, reliability B7 related to shape acquisition method, reliability B8 related to phase, reliability B9 related to magnitude of motion, and reliability B10 related to artifacts, each from the perspective of how reliable the shape is, and calculates the reliability of the shape based on the reliability B1 to B10. Similarly, the calculation function 155b calculates reliability C1 related to the image type, reliability C2 related to the imaging conditions, reliability C3 related to the blood vessel type, and reliability C4 related to the phase from the perspective of how reliable the properties are, and calculates the reliability of the properties based on the reliabilities C1 to C4. Similarly, the calculation function 155b calculates reliability D1 related to the image type, reliability D2 related to the imaging conditions, reliability D3 related to the blood vessel type, and reliability D4 related to the phase from the perspective of how reliable the fluid information is, and calculates the reliability of the fluid based on the reliabilities D1 to D4. Then, the calculation function 155b calculates the reliability of the WSS (total reliability) based on the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid.

[0041] The reliability of the calculation conditions shown in FIG. 3A is an example of a first reliability related to the calculation conditions. The reliability of the shape shown in FIG. 3B is an example of a second reliability related to the shape. The reliability of the property shown in FIG. 3B is an example of a third reliability related to the property. The reliability of the fluid shown in FIG. 3B is an example of a fourth reliability related to the fluid.

[0042] FIG. 4 shows an example of a method for calculating reliability A1 related to mesh size. For example, when calculating WSS or the like using the finite element method, the shape and fluid are first decomposed into meshes. When determining the mesh size, the average and variance of the mesh size are often determined, and the division is performed so that the mesh size approaches these values. Generally, the smaller the mesh size (average) and the smaller the variance (variation), the higher the accuracy of the analysis. As described above, the calculation function 155b can calculate reliability A1 based on the average and variance of the set mesh size.

[0043] For example, the memory 152 stores a plurality of settings for calculation conditions, each combining the average and variation of mesh size. For example, as shown in FIG. 4, the memory 152 stores six settings: "mean:≦0.001 mm³, skewness:>X, likelihood:>Y," "mean:≦0.001 mm³, skewness:≦X, likelihood:≦Y," "mean:>0.001 mm³, skewness:≦X, likelihood:≦Y," "mean:>0.001 mm³, skewness:≦X, likelihood:≦Y," "mean:>0.1 mm³, skewness:>X, likelihood:>Y," and "mean:>0.1 mm³, skewness:≦X, likelihood:≦Y." Note that X and Y in FIG. 4 are predetermined thresholds. The setting function 155a selects one of the six settings stored in the memory 152 and sets it as the analysis condition. Then, the calculation function 155b calculates the reliability A1 based on the analysis conditions set by the setting function 155a. For example, if the average mesh size is smaller than 0.001 mm3, the skewness is larger than X, and the likelihood is larger than Y, the calculation function 155b calculates "reliability A1=100".

[0044] While FIG. 4 shows mesh size as an example, it may be expressed not only by size but also by volume, surface area, weight, length of one side of the mesh, etc. Furthermore, the volume, surface area, and weight are not limited to the average, but may be expressed as the maximum value of all meshes, the minimum value of all meshes, the median value of all meshes, etc. Furthermore, the length of one side of the mesh is not limited to the average, but may be expressed as the average value of the maximum values ​​of each mesh, the median value of the maximum values ​​of each mesh, the average value of the minimum values ​​of each mesh, the median value of the minimum values ​​of each mesh, the average value of the average values ​​of each mesh, the median value of the average values ​​of each mesh, etc. Similarly, while FIG. 4 shows the variation in mesh size using skewness and likelihood, it is not limited to these and various modifications are possible as long as they are indices related to the variation in mesh size. For example, variance may be used as the variation in mesh size.

[0045] In addition, various measures may be taken to set the mesh size. Taking such circumstances into consideration, the calculation function 155b may add points to the reliability A1.

[0046] For example, medical images such as CT images may be affected by the subject's breathing or pulsation. One possible method is to detect areas of complex movement in advance from medical images and then apply a fine mesh to those areas. An example of an area of ​​complex movement is an area that is suddenly compressed, stretched, or bent. For example, areas of complex movement can be detected by capturing multiple frames of CT images over time and comparing the frames. That is, the calculation function 155b may detect areas of significant movement based on time-series medical images and calculate the reliability of the mesh shape based on the detection results. Alternatively, vascular branches or areas of complex movement can be predefined based on past knowledge. Hereinafter, this mesh-defining method will be referred to as Method M11. By applying Method M11 to the mesh definition, it is possible to reduce the amount of calculation required during analysis while improving the accuracy of the analysis.

[0047] Another possible method is to detect areas of complex movement from medical images, calculate the complexity of the movement at each position, and change the mesh size according to that complexity. Hereinafter, this mesh setting method will be referred to as Method M12. Setting the mesh using Method M12 can improve the accuracy of the analysis while reducing the amount of calculation required during analysis.

[0048] Furthermore, for meshes set for fluids such as blood, a method of changing the mesh size can be considered, taking into account the flow state. For example, the probability of turbulence occurring at each position within a blood vessel can be calculated based on blood vessel shape, such as vessel diameter, and fluid information, such as blood viscosity and flow velocity, and a finer mesh can be set for areas where turbulence is likely to occur. That is, the calculation function 155b can detect areas where turbulence is likely to occur and calculate the reliability of the mesh size based on the detection results. Hereinafter, this mesh setting method will be referred to as method M13. Setting the mesh using method M13 can reduce the amount of calculation during analysis while improving the accuracy of the analysis.

[0049] Another possible method is to repeatedly set the mesh until the analysis results converge. For example, a coarse mesh is first set and the analysis is then performed repeatedly, with the mesh gradually refined until the difference in the analysis results between each iteration is less than a threshold value. Specifically, each time the mesh is reset and an analysis is performed to calculate the WSS, the difference from the WSS calculated immediately before is calculated, and the mesh resetting and analysis are repeated until the difference falls below a threshold value. Hereinafter, this mesh setting method will be referred to as Method M14. Setting the mesh using Method M14 can improve the accuracy of the analysis. Note that when using the method of repeatedly setting the mesh, it is not necessary to continue resetting the mesh and performing repeated analyses for the entire analysis system. For example, after resetting the mesh and performing repeated analyses, a region where the difference in the analysis results between each iteration is less than a threshold value can be identified. It is possible to avoid resetting the mesh for the identified region where the difference in the analysis results is small, i.e., the converged region, and to reset the mesh only for the region other than the converged region in the next iteration.

[0050] For example, as shown in FIG. 4, the calculation function 155b adds "+10" points when method M11 is added to the mesh size setting, adds "+10" points when method M12 is added, adds "+50" points when method M13 is added, and adds "+50" points when method M14 is added. For example, the memory 152 stores five settings for calculation conditions: "Method M11 added," "Method M12 added," "Method M13 added," "Method M14 added," and "No additional method." The setting function 155a selects one of the five settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability A1 based on the analysis conditions set by the setting function 155a. For example, if the average mesh size is greater than 0.1 mm3, the skewness is greater than X, the likelihood is greater than Y, and method M13 has been added, the calculation function 155b calculates "reliability A1 = 10 + 50 = 60".

[0051] The calculation function 155b may set an upper limit for the reliability A1. For example, the calculation function 155b sets the upper limit to "100", and when the total value exceeds "100" due to the addition of points in methods M11 to M14, the calculation function 155b sets the reliability A1 to "100". That is, the calculation function 155b calculates a value of "0 to 100" as the reliability A1.

[0052] Furthermore, while Figure 4 illustrates the reliability corresponding to each setting using specific numerical values, these numerical values ​​are merely examples and can be adjusted as desired. The reliability corresponding to each setting can be determined empirically, determined based on the number of papers or impact factor, or set in advance using other methods. The reliability corresponding to each setting can also be adjusted appropriately through user input or machine learning techniques. Furthermore, the reliability does not have to be a numerical value. For example, the reliability can be a rank or classification such as "high / medium / low" or "sufficient / insufficient."

[0053] Furthermore, the calculation function 155b may calculate the reliability for each position in the target region, or may calculate a single reliability for the entire target region. When calculating the reliability for each position, the calculation function 155b calculates, for example, the spatial distribution of the reliability A1 in the vascular region included in the CT image. When calculating a single reliability, the calculation function 155b calculates, for example, the reliability for each position in the vascular region included in the CT image, and calculates a representative value such as the average value or the minimum value as the reliability A1.

[0054] FIG. 5 shows an example of a method for calculating the reliability A2 related to the shape of a mesh. For example, when setting a three-dimensional mesh, it is possible to select a tetrahedral mesh or a hexahedral mesh. For example, when setting a two-dimensional mesh, it is possible to select a triangular mesh or a rectangular mesh. Note that a two-dimensional mesh may also be set for a CT image (volume data). For example, blood vessels may be segmented from a CT image, and a two-dimensional mesh may be set along the inner wall of the blood vessel for analysis. It is known that the accuracy of the analysis varies depending on the shape of such a mesh. Therefore, the calculation function 155b can calculate the reliability A2 based on the shape of the set mesh.

[0055] For example, the memory 152 stores a plurality of mesh shapes as settings for the calculation conditions. For example, as shown in FIG. 5, the memory 152 stores four settings: "tetrahedron," "hexahedron," "triangle," "octahedron," "dodecahedron," and "rectangle." The setting function 155a selects one of the four settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability A2 based on the analysis condition set by the setting function 155a. For example, if "rectangle" is set as the analysis condition, the calculation function 155b calculates "reliability A2=90."

[0056] The calculation function 155b may also add points to the reliability A2. For example, if the mesh is changed for each region, the calculation function 155b adds a score of "+10." Specifically, it is possible to select a mesh shape with high analytical accuracy for regions with complex movement or regions where turbulence is likely to occur, and select a mesh shape with low computational load for other regions. Regions with complex movement can be detected, for example, by capturing CT images in a time series and identifying regions where the amount of movement or the change in movement direction of the vascular wall per unit time is large. That is, the calculation function 155b may detect regions with large movement based on the time series of medical images and calculate the reliability of the mesh shape based on the detection results. On the other hand, regions where turbulence is likely to occur include, for example, blood vessel bifurcations and plaque areas with wall surfaces perpendicular to the blood flow direction. Geometric models of regions where turbulence is likely to occur are stored in advance, and when a blood vessel is divided into sections, regions with high similarity to the pre-stored geometric models can be detected as regions where turbulence is likely to occur. That is, the calculation function 155b may detect an area where turbulence is likely to occur, and calculate the reliability of the mesh shape based on the detection result. Furthermore, even for meshes of the same shape, the calculation function 155b may set different reliability levels when the mesh is set for the shape of a blood vessel or the like and when the mesh is set for a fluid such as blood.

[0057] Note that, as with the reliability A1, the calculation function 155b may set an upper limit for the reliability A2. Also, as with the case of FIG. 4, the reliability corresponding to each setting is explained using specific numerical values ​​in FIG. 5, but these numerical values ​​are merely examples and various modifications are possible. Also, the calculation function 155b may calculate the reliability A2 for each position in the target area, or may calculate a single reliability A2 for the entire target area. The same applies to various reliabilities described below.

[0058] FIG. 6 shows an example of a method for calculating the reliability A3 related to mesh quality. For example, mesh quality can be classified into first-order meshes and second-order or higher-order meshes. For example, a first-order mesh of a tetrahedron uses the four vertices of the tetrahedron as calculation points. On the other hand, a second-order mesh of a tetrahedron has calculation points on the edges and faces of the tetrahedron in addition to the four vertices of the tetrahedron, allowing for more detailed calculations. It is known that the accuracy of analysis varies depending on the quality of such meshes. Therefore, the calculation function 155b can calculate the reliability A3 based on the set mesh quality.

[0059] For example, the memory 152 stores multiple mesh qualities as settings for the calculation conditions. For example, as shown in FIG. 6, the memory 152 stores two settings, such as “primary” and “secondary.” The setting function 155a selects one of the two settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability A3 based on the analysis condition set by the setting function 155a. For example, if “primary” is set as the analysis condition, the calculation function 155b calculates the reliability A3 as “80.” The calculation function 155b may also add points to the reliability A3. For example, if the mesh is changed in an area where complex movement occurs or an area where turbulence is likely to occur, the calculation function 155b may add “+10.” That is, the calculation function 155b may detect areas where turbulence is likely to occur and calculate the reliability related to the mesh quality based on the detection results. The calculation function 155b may also detect areas with large movements based on time-series medical images, and calculate the reliability of the mesh quality based on the detection results.

[0060] An example of a method for calculating reliability A4 related to time resolution is shown in Figure 7. Generally, fluid analysis for calculating WSS, etc. is a four-dimensional analysis in which a time axis is added to three spatial axes. It is known that analysis accuracy improves by performing the analysis with finer time resolution. As described above, the calculation function 155b can calculate reliability A4 based on the set time resolution.

[0061] However, as the time resolution is made finer, the analysis accuracy reaches an upper limit at a certain stage, and further finer setting of the time resolution only increases the amount of calculation, and the analysis accuracy does not improve. Therefore, it is preferable to set the time resolution appropriately, taking into consideration the balance between the amount of calculation and the analysis accuracy.

[0062] Furthermore, in principle, there is no upper limit to the temporal resolution, and any value can be set. For example, it is possible to set the temporal resolution to a value finer than the frame rate at which the medical images, such as CT images, used for analysis are acquired. In other words, it is possible to perform analysis by interpolating between frames using time-series medical images acquired at a certain frame rate as boundary conditions. However, setting a temporal resolution that is too fine compared to the frame rate of the medical images increases the amount of calculation while not improving the accuracy of the analysis. Therefore, it is preferable to set an appropriate temporal resolution according to the frame rate of the medical images.

[0063] For example, the memory 152 stores multiple settings for the time resolution ΔT as settings for the calculation conditions. For example, as shown in FIG. 7, the memory 152 stores three settings: ">1.0×10 sec," "≦1.0×10 sec, >1.0×10 sec," and "≦1.0×10 sec." The setting function 155a selects one of the three settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability A4 based on the analysis condition set by the setting function 155a. For example, if a time resolution coarser than "1.0×10 sec" is set as the analysis condition, the calculation function 155b calculates the reliability A4 as "10."

[0064] Furthermore, the calculation function 155b may add points to the reliability A4. For example, the flow velocity within a blood vessel is not constant, but varies depending on the position and time. Furthermore, the blood vessel itself may move due to the influence of breathing, pulsation, etc., and the blood vessel movement speed is also not constant. To maintain the accuracy of the analysis, it is preferable to set the time resolution finer as the velocity of the blood flow or blood vessel increases. However, if the time resolution is set finer uniformly, the amount of calculation in the analysis increases. Therefore, by varying the time resolution according to the flow velocity within the blood vessel or the blood vessel movement speed, the amount of calculation can be reduced while improving the accuracy of the analysis.

[0065] For example, it is conceivable to use time-series CT images to monitor blood vessels and the blood flow within them, and to set the time resolution finely only for the positions and times when the blood flow exceeds a certain speed. Hereinafter, this method of setting the time resolution will be referred to as Method M21.

[0066] Furthermore, the profile of blood velocity flowing through each blood vessel, such as the coronary artery, is generally clinically determined. For example, it is known that blood flow velocity is fastest in the early diastolic phase of the cardiac phase. Therefore, it is conceivable to obtain cardiac phase information using an electrocardiograph or time-series CT images, and then finely set the time resolution only for the early diastolic phase. Hereinafter, this method of setting the time resolution will be referred to as Method M22.

[0067] For example, as shown in FIG. 7, the calculation function 155b adds a score of "+10" when method M21 is added to the time resolution setting, and adds a score of "+1" when method M22 is added. For example, the memory 152 stores three settings for calculation conditions: "method M21," "method M22," and "no additional method." The setting function 155a selects one of the three settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability A4 based on the analysis condition set by the setting function 155a. For example, when a time resolution coarser than "1.0 × 10 sec" is set and method M21 is added, the calculation function 155b calculates the reliability A4 as "10 + 10 = 20."

[0068] An example of a method for calculating the reliability A5 related to a calculation model is shown in Figure 8. Various calculation models are known that can be used to perform fluid analysis and obtain WSS, and it is generally known that the more complex the calculation model and the greater the amount of calculation required, the higher the accuracy of the analysis. Therefore, the calculation function 155b can calculate the reliability A5 based on the set calculation model.

[0069] One example of a computational model that can be used to obtain WSS is the 0D model. The 0D model is a method of analysis in which blood vessels are treated as circuits based on factors such as the difficulty of fluid flow, and is sometimes called an equivalent circuit model or a lumped model. Furthermore, even within the 0D model, the accuracy of analysis differs between circuits that model the entire body and circuits that model only the target blood vessels. The 0D model makes it possible to obtain highly accurate analysis results with a relatively small amount of calculation.

[0070] Another example of a computational model is a simulation model based on fluid-structure interaction (FSI). Furthermore, within FSI, the accuracy of the analysis differs depending on whether the Arbitrary-Lagrangian-Eulerian (ALE) method or the Immerse-Boundary (IB) method is used. In addition to 0D models and FSI, various other computational models are known, and it is possible to use, for example, a computational model of only structure or only fluid.

[0071] For example, the memory 152 stores a plurality of calculation models as settings for calculation conditions. For example, as shown in FIG. 8, the memory 152 stores six settings: a "0D model using a circuit that models the entire body," a "0D model using a circuit that models only the target blood vessels," an "FSI using ALE," an "FSI using IB," a "calculation model of structure only," and a "calculation model of fluid only." The setting function 155a selects one of the six settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability A5 based on the analysis conditions set by the setting function 155a.

[0072] In any calculation model, boundary conditions are often set to improve calculation accuracy. The boundary conditions in fluid analysis can be roughly divided into, for example, conditions F1 that can be measured from an image of the subject, conditions F2 that are obtained by performing further calculations based on information obtained from the image of the subject, conditions F3 that are conditions of values ​​other than the image of the subject, universal conditions F4, and conditions F5 that are obtained for the group to which the subject belongs.

[0073] Here, the type of condition F1 that can be measured from an image of the subject is, for example, an image measurement value such as blood vessel diameter, or a blood flow measurement value such as blood flow velocity or flow rate. Furthermore, the type of condition F3 that is a value other than the image of the subject is, for example, information obtained by a blood test or a medical interview. Furthermore, the universal condition F4 is, for example, an average value for all people or the general public. Furthermore, the type of condition F5 that can be obtained regarding a group to which the subject belongs is, for example, an average value among people with the same or similar symptoms as the subject.

[0074] Condition F2, which is obtained by further calculation based on information obtained from an image of the subject, is, for example, a parameter obtained based on image measurement values ​​or blood flow measurement values. For example, when blood vessel diameter, flow velocity, flow rate, etc. are measured, parameters such as energy, passing blood volume (mass), shear stress, ventricular rate of change, and ejection fraction (EF) can be calculated secondarily. Hereinafter, parameters obtained based on image measurement values ​​or blood flow measurement values ​​are also referred to as first parameters.

[0075] Other examples of the condition F2 include parameters obtained from perfusion (perfusion calculation), such as perfusion area, perfusion volume, perfusion rate, perfusion amount, and distribution of each vascular branch. Hereinafter, parameters obtained based on perfusion are also referred to as second parameters.

[0076] Another example of condition F2 is a parameter obtained from medical images of multiple time phases. Examples of such parameters include time change, time integral, time derivative, time difference, spatiotemporal differential integral, mean variation, and time variation of variance variation. Hereinafter, a parameter obtained based on medical images of multiple time phases will also be referred to as a third parameter.

[0077] There are many other possible methods for calculating condition F2. For example, parameters obtained from images or parameters obtained by performing calculations on parameters obtained from images can be first obtained, and then a different parameter obtained by multiplying these parameters can be used as condition F2. An example of such a different parameter is a parameter obtained by dividing the volume of blood passing through the end point of the analysis system by the cross-sectional area of ​​the blood vessel.

[0078] The calculation function 155b may add points to the reliability A5 based on such boundary conditions. That is, the memory 152 stores a plurality of types of boundary conditions as settings for the calculation conditions. For example, the memory 152 stores conditions F1 to F5 as a plurality of settings. For another example, the memory 152 stores a first parameter, a second parameter, and a third parameter as a plurality of settings. The setting function 155a selects at least one of the plurality of settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability A5 based on the analysis condition set by the setting function 155a. For example, the calculation function 155b calculates the reliability A5 based on the setting set as the analysis condition from among the plurality of calculation models shown in FIG. 8 and the setting set as the analysis condition from among the first parameter, the second parameter, and the third parameter.

[0079] After calculating the reliability A1 to A5, the calculation function 155b calculates the reliability of the calculation condition based on the reliability A1 to A5. For example, the calculation function 155b calculates each of the reliability A1 to A5 within a numerical range of "0 to 100" and calculates the average of the reliability A1 to A5 as the reliability of the calculation condition. The calculation function 155b may also calculate a weighted average based on the reliability A1 to A5 as the reliability of the calculation condition. For example, if the mesh size has a greater effect on the reliability than other indicators, the calculation function 155b assigns a larger weight to the reliability A1 and calculates an average value as the reliability of the calculation condition.

[0080] The reliability of the calculation condition may be a single value calculated for the entire vascular region, or may be a value calculated for each type of blood vessel. Alternatively, the reliability of the calculation condition may be a value calculated for each position (for example, each pixel). That is, the reliability of the calculation condition may be calculated as a spatial distribution of the reliability.

[0081] Next, the reliability of the shape based on the reliability B1 to B10 will be described. First, a method for calculating the reliability B1 related to the type of image will be described.

[0082] Although the above description has mainly focused on CT images acquired by the X-ray CT device 110, various medical images can be used to acquire blood vessel shapes. For example, blood vessel shapes can be acquired from medical images such as MR images, X-ray angiograms, intravascular ultrasound (IVUS) images, optical coherence tomography (OCT) images, and intracardiac echocardiography (ICE) images, in addition to CT images. Blood vessel shapes can also be acquired from images included in medical records or analytical images such as perfusion images. Specifically, blood vessel shapes can be acquired by performing blood vessel segmentation based on these medical images.

[0083] X-ray angiographic images are usually two-dimensional images, and the blood vessel shapes obtained from X-ray angiographic images are also two-dimensional information. However, it is also possible to obtain three-dimensional blood vessel shapes by rotating the X-ray tube and detector around the subject to collect multiple images at different shooting angles, or by collecting two images at different shooting angles in a biplane system.

[0084] There are also cases where blood vessel shapes are acquired without using medical images. For example, general blood vessel shapes based on literature values ​​are defined in advance for each condition, such as weight, height, and body part. Then, based on patient information acquired from each department system 130, one of the defined blood vessel shapes is selected and used for analysis.

[0085] FIG. 9 shows an example of a method for calculating the reliability B1 related to the type of image. For example, the memory 152 stores a plurality of methods for acquiring a blood vessel shape as shape settings. For example, as shown in FIG. 9, the memory 152 stores seven settings: "CT," "MRI," "IVUS," "OCT," "X-ray angiography (2D)," "X-ray angiography (3D)," and "no image used." The setting function 155a selects one of the seven settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability B1 based on the analysis condition set by the setting function 155a. For example, when acquiring a blood vessel shape based on IVUS, the calculation function 155b calculates "reliability B1 = 30."

[0086] Furthermore, the calculation function 155b may add points to the reliability B1. For example, when segmenting blood vessels from a medical image, a method is known in which each pixel (voxel or pixel) in the medical image is not exclusively classified into "fluid (blood, etc.)" and "solid (blood, etc.)," ​​but rather a proportion of "fluid-likeness" and "solid-likeness" is set for each pixel. Hereinafter, this method of acquiring the blood vessel shape will be referred to as method M31. Method M31 is realized, for example, by defining a viscosity coefficient and an elasticity coefficient for each pixel.

[0087] Method M31 makes it possible to represent a state in which a fluid and a solid coexist within a single pixel. In other words, the spatial resolution of medical images is finite, and a single pixel may contain both a fluid and a solid. Method M31 makes it possible to represent such a state in which a fluid and a solid coexist, thereby improving the accuracy of analysis. Note that method M31 is often used when IB is used as a computational model.

[0088] Also, a method for acquiring blood vessel shapes using multiple types of medical images is known. For example, IVUS images can acquire more detailed shapes than other types of medical images, but they have the property of being unable to acquire shapes other than those of blood vessels into which an IVUS catheter is inserted. For example, if an IVUS catheter is inserted into a coronary artery, shapes other than those of the coronary artery cannot be acquired from the IVUS image. Therefore, a method can be considered in which the overall shape of the blood vessel is acquired based on CT images, etc., and then combined with the shape acquired from the IVUS image. Hereinafter, this method for acquiring blood vessel shapes will be referred to as method M32. Method M32 effectively utilizes collected medical images to acquire blood vessel shapes over a wide area with high accuracy, thereby improving the accuracy of analysis.

[0089] The method for synthesizing blood vessel shapes is not particularly limited, but one possible method is to align the blood vessel shapes using characteristic features that appear in the medical images as a reference. Specific examples of such characteristic features include plaque and calcification within blood vessels, and curved and branched portions of blood vessels. To improve the accuracy of alignment, it is preferable to use features that appear clearly in each of the different types of medical images as a reference. Synthesis may be performed between blood vessel shapes segmented from each medical image, or the blood vessel shapes may be segmented after synthesis between the medical images.

[0090] For example, as shown in FIG. 9, the calculation function 155b adds a score of "+10" when method M31 is added to the blood vessel shape acquisition method, and adds a score of "+10" when method M32 is added. For example, the memory 152 stores three settings for shape: "method M31," "method M32," and "no additional method." The setting function 155a selects one of the three settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability B1 based on the analysis condition set by the setting function 155a. For example, when a CT image is used to acquire the blood vessel shape and method M31 is added, the calculation function 155b calculates the reliability B1 as "80+10=90."

[0091] An example of a method for calculating reliability B2 related to imaging conditions is shown in FIG. 10. Four examples of medical images, "CT," "MR," "IVUS," and "OCT," are shown in FIG. 10. That is, even for the same type of medical image, the image quality varies depending on the imaging conditions, and the accuracy of the shape acquired based on the medical image also varies. Therefore, the calculation function 155b can calculate reliability B2 based on the set imaging conditions.

[0092] For example, in the case of CT images, imaging conditions related to the calculation of reliability B2 include "device," "imaging time," "filter," and "reconstruction interval." "Device" varies depending on, for example, the manufacturer and model number. Regarding "imaging time," generally, the longer the imaging time, the better the image quality. However, for moving areas such as the chest, it may be preferable to complete imaging in a short time. "Filter" is the type of filter used to correct projection data when reconstructing a CT image using, for example, filtered back-projection (FBP). "Reconstruction interval" is the type of algorithm used when reconstructing a CT image using, for example, iterative reconstruction (IR).

[0093] For example, the memory 152 stores a plurality of imaging conditions for collecting CT images as settings for the shape. For example, the memory 152 stores two settings, "Device Q11" and "Device Q12," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The memory 152 also stores two settings, "Imaging time: >125 msec" and "Imaging time: ≦125 msec," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The calculation function 155b then calculates the reliability B2 based on the analysis condition set by the setting function 155a. For example, if "Device Q11" and "Imaging time: >125 msec" are set as the analysis conditions, the calculation function 155b calculates the reliability B2 as "30 + 10 = 40." Furthermore, the calculation function 155b adds "+5" points when a predetermined filter R1 is used as the "filter." Furthermore, the calculation function 155b adds "+1" points when a predetermined function T1 is used as the "reconstruction function."

[0094] Note that FIG. 10 is merely an example. For example, in the case of a CT image, the higher the dose (exposure dose) during imaging, the better the image quality, such as SNR. Therefore, the calculation function 155b may perform calculations such that the reliability B2 increases as the dose increases. In addition, the image quality of a CT image changes depending on various imaging conditions, such as the subject's body thickness, the region to be imaged, the imaging method (e.g., helical scan / non-helical scan), and the helical pitch. The calculation function 155b may calculate the reliability B2 taking these imaging conditions into consideration.

[0095] In the case of MR images, imaging conditions related to the calculation of reliability B2 include "device," "imaging time," and "static magnetic field." "Device" varies depending on, for example, the manufacturer and model number. Regarding "imaging time," the longer the imaging time, the better the image quality. However, since a long imaging time places a burden on the subject, it is preferable to set an appropriate imaging time. "Static magnetic field" is the strength of the magnetic field used for imaging, and generally, the stronger the magnetic field, the better the image quality.

[0096] For example, the memory 152 stores a plurality of imaging conditions for collecting MR images as shape settings. For example, the memory 152 stores two settings, "Device Q21" and "Device Q22," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The memory 152 also stores two settings, "Imaging time: >30 min" and "Imaging time: ≦30 min," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The calculation function 155b then calculates the reliability B2 based on the analysis condition set by the setting function 155a. For example, if "Device Q21" and "Imaging time: >30 min" are set as the analysis conditions, the calculation function 155b calculates the reliability B2 as "30 + 10 = 40." Furthermore, the calculation function 155b adds "+1" when a magnetic field of "8 T" or more is set as the "static magnetic field."

[0097] For IVUS and OCT images, imaging conditions related to the calculation of reliability B2 include the "device," "withdrawal speed," and "rotation speed." The "device" varies depending on, for example, the manufacturer and model number. The "withdrawal speed" is the speed at which the IVUS catheter or OCT catheter moves along the centerline of the blood vessel. Generally, the slower the withdrawal speed, the better the image quality, but the longer the imaging time.

[0098] The "rotation speed" is the speed at which the IVUS catheter or OCT catheter rotates around the centerline of the blood vessel as the rotation axis. Here, if the rotation speed is excessively high, the image quality will deteriorate. On the other hand, if the rotation speed is excessively low, the blood vessel will move due to the influence of pulsation, etc., and the correct blood vessel shape at a specific time phase may not be captured. Furthermore, if the rotation speed is low and the extraction speed is high, the spatial resolution of the image will deteriorate. Therefore, it is preferable that the rotation speed be set to an appropriate value depending on the extraction speed. Note that it is also possible to associate a predetermined rotation speed with each extraction speed value.

[0099] For example, the memory 152 stores a plurality of imaging conditions for collecting IVUS images as shape settings. For example, the memory 152 stores two settings, "Device Q31" and "Device Q32," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The memory 152 also stores two settings, "Extraction speed: <1.5 mm / sec" and "Extraction speed: ≧1.5 mm / sec," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The memory 152 also stores three settings, "Rotation speed: >20 frames / sec," "Rotation speed: <20 frames / sec, ≧10 frames / sec," and "Rotation speed: <10 frames / sec," and the setting function 155a selects one of these three settings and sets it as the analysis condition. Then, the calculation function 155b calculates the reliability B2 based on the analysis conditions set by the setting function 155a. For example, if "Device Q31" is set as the analysis condition, the withdrawal speed is less than "1.5 mm / sec", and the rotation speed is less than "10 frames / sec", the calculation function 155b calculates "reliability B2=30+10+0=40".

[0100] For example, the memory 152 stores multiple imaging conditions for collecting OCT images as shape settings. For example, the memory 152 stores two settings, "Device Q41" and "Device Q42," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The memory 152 also stores two settings, "Extraction speed: <20 mm / sec" and "Extraction speed: ≧20 mm / sec," and the setting function 155a selects one of these two settings and sets it as the analysis condition. The memory 152 also stores three settings, "Rotation speed: >100 frames / sec," "Rotation speed: <100 frames / sec, ≧50 frames / sec," and "Rotation speed: <50 frames / sec," and the setting function 155a selects one of these three settings and sets it as the analysis condition. Then, the calculation function 155b calculates the reliability B2 based on the analysis conditions set by the setting function 155a. For example, if "Device Q41" is set as the analysis condition, the withdrawal speed is less than "20 mm / sec", and the rotation speed is less than "50 frames / sec", the calculation function 155b calculates "reliability B2=30+15+0=45".

[0101] Next, a method for calculating the reliability B3 related to the type of blood vessel will be described. For example, in the case of coronary arteries, blood vessels can be classified according to the AHA classification, as shown in FIG. 11A. Furthermore, there are trends in the thickness and magnitude of movement of blood vessels depending on the type of blood vessel, and blood vessels whose blood vessel shapes are easy to acquire and blood vessels whose blood vessel shapes are difficult to acquire are generally determined. Therefore, the calculation function 155b can calculate the reliability B3 based on the set type of blood vessel.

[0102] Although the entire coronary artery may be analyzed, in many cases analysis is performed by selecting only a portion of the blood vessels. For example, analysis may be performed by selecting only blood vessels that will cause severe symptoms if stenosis occurs, such as "#5 (LMT)" shown in Figure 11A. In addition, analysis may be performed by selecting only blood vessels in which calcification or plaque has been confirmed in medical images. In addition, analysis may be performed by excluding blood vessels that are expected to have large movements based on past findings.

[0103] FIG. 11B shows an example of a method for calculating the reliability B3 related to the type of blood vessel. For example, the memory 152 stores multiple blood vessel types as shape settings. For example, as shown in FIG. 11B, the memory 152 stores multiple settings such as "#1," "#2," "#3," "#4," "#5," "#6," "#7," "#8," "#9," "#10," "#11," "#12," "#13," "#14," and "#15." The setting function 155a selects at least one of the multiple settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability B3 based on the analysis condition set by the setting function 155a.

[0104] In addition, when multiple types of blood vessels are defined as analysis conditions, the calculation function 155b may calculate the reliability for each type of blood vessel separately, or may calculate a single reliability. For example, when "#1" and "#2" are defined as analysis conditions, the calculation function 155b calculates the reliability B3 for "#1" and the reliability B3 for "#2". Alternatively, when "#1" and "#2" are defined as analysis conditions, the calculation function 155b calculates only the reliability B3 for "#1 and #2". For example, in the case shown in FIG. 11B, the calculation function 155b calculates the reliability B3 for "#1 and #2" as "reliability B3 = (10 + 20) / 2 = 15".

[0105] Next, a method for calculating the reliability B4 related to the blood vessel shape will be described. Generally, the thinner the blood vessel, the more difficult it is to acquire the blood vessel shape. For example, when acquiring the shape of a coronary artery based on a CT image, the smaller the blood vessel diameter is relative to the spatial resolution of the CT image, the lower the accuracy of the acquired blood vessel shape. In particular, it is difficult to acquire the blood vessel shape for a blood vessel with a diameter comparable to the spatial resolution of the CT image. As described above, the calculation function 155b can calculate the reliability B4 based on the set blood vessel shape.

[0106] For example, the memory 152 stores a plurality of parameters indicating blood vessel shape as shape settings. For example, as shown in FIG. 12, the memory 152 stores four settings: "blood vessel shape: >30 [mm]," "blood vessel shape: ≦30 [mm], >10 [mm]," "blood vessel shape: ≦10 [mm], >2.5 [mm]," and "blood vessel shape: ≦2.5 [mm]." That is, the memory 152 stores blood vessel size classifications as shape settings. The setting function 155a selects one of the settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability B4 based on the analysis conditions set by the setting function 155a.

[0107] Since the thickness of a blood vessel varies depending on the position, the calculation function 155b may calculate the reliability B4 for each position of the blood vessel, or may calculate a single reliability B4 for the entire blood vessel based on, for example, the average value of the blood vessel thickness at each position. The calculation function 155b may also calculate the reliability B4 for each type of blood vessel. Furthermore, in the case of a complex blood vessel shape with many branches and curves, the accuracy of the blood vessel shape acquired based on CT images or the like decreases. Therefore, the calculation function 155b may calculate the reliability B4 by taking into account the number of branches, the curvature of the blood vessel, and the like in addition to the blood vessel thickness.

[0108] Next, we will explain how to calculate the reliability B5 related to intravascular structures. Intravascular structures include non-artificial objects such as calcification and plaque within blood vessels, as well as artificial objects such as stents placed within blood vessels. It is known that the presence of such intravascular structures within blood vessels reduces the accuracy of the acquired shape. In other words, the presence of intravascular structures makes the shape of the blood vessel more complex, making it difficult to acquire an accurate shape. Furthermore, when acquiring a blood vessel shape based on CT images, if intravascular structures are included in the imaging range, metal artifacts may occur, reducing the accuracy of the acquired shape.

[0109] For example, the memory 152 stores multiple conditions regarding the presence or absence and type of intravascular structures as shape settings. For example, as shown in FIG. 13, the memory 152 stores four settings: "none," "calcification," "plaque," and "artificial object." The setting function 155a selects one of the four settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability B5 based on the analysis condition set by the setting function 155a.

[0110] In addition, when intravascular structures such as plaque are present, there are known methods for acquiring the blood vessel shape assuming that the intravascular structures are absent, and methods for detecting the shape of the intravascular structures as well. Detecting the shape of the intravascular structures requires the execution of a detection algorithm, which increases the amount of calculation, but improves the accuracy of the analysis. Hereinafter, the method for detecting the shape of the intravascular structures will be referred to as method M41. When method M41 is added, the calculation function 155b may add "+10" points, for example, as shown in FIG. 13.

[0111] Furthermore, the calculation function 155b may calculate the reliability B5 for each position. For example, the calculation function 155b may calculate the reliability B5 for the vicinity of the intravascular structure and the reliability B5 for other regions, respectively. For example, the calculation function 155b may calculate the intravascular structure for each pixel. That is, the calculation function 155b may detect the position and type of the intravascular structure, and calculate the reliability related to the intravascular structure for each position according to the type of the intravascular structure.

[0112] An example of a method for calculating reliability B6 related to intermediate calculation results is shown in Figure 14. When calculating index values ​​such as WSS using CFD, calculations are generally repeated until the final result is obtained. Even if the final result is a valid value, it does not necessarily mean that it is an appropriate analysis result. In other words, it is possible that a valid value was calculated by chance, rather than as a result of an appropriate analysis.

[0113] In a proper analysis, the initial value is gradually adjusted in repeated calculations until the final result is reached. Therefore, the calculation function 155b can calculate the reliability B6 by evaluating the intermediate calculation results.

[0114] For example, intermediate calculation results can be evaluated by comparing various CFD-related parameters with literature values. For example, first, average literature values ​​are obtained for blood vessel pressure, blood vessel stiffness, blood flow velocity, blood vessel movement, etc., for blood vessel #8 shown in Figure 11A. Next, the ratio of the intermediate calculation result with the largest difference from the average literature value to the average literature value is calculated. If the calculated ratio is close to "100%," it can be evaluated that the analysis is being performed appropriately.

[0115] For example, the memory 152 stores multiple ratios of intermediate calculation results to the average value of the literature values ​​as shape settings. For example, as shown in FIG. 14, the memory 152 stores five settings: "500% or more of the average value of the literature values," "150% to 500% of the average value of the literature values," "75% to 150% of the average value of the literature values," "20% to 75% of the average value of the literature values," and "20% or less of the average value of the literature values." The setting function 155a selects one of the five settings stored in the memory 152 and sets it as an analysis condition. For example, after performing an analysis to calculate the WSS, the setting function 155a acquires an intermediate calculation result of the analysis, determines which of the five settings the result corresponds to, and sets the result of the determination as the analysis condition. The calculation function 155b then calculates the reliability B6 based on the analysis conditions set by the setting function 155a.

[0116] 14, the calculation function 155b may calculate the reliability B6 for each of the vascular pressure, vascular stiffness, blood flow velocity, and vascular movement, or may calculate a single reliability B6. For example, the setting function 155a may specify the parameter among the vascular pressure, vascular stiffness, blood flow velocity, and vascular movement that has the largest difference from the average value of the literature value to determine the analysis conditions, and the calculation function 155b may calculate the reliability B6 for only that parameter.

[0117] An example of a method for calculating the reliability B7 related to the shape acquisition method is shown in Figure 15. Blood vessel shapes are often acquired by segmenting blood vessels from medical images. Here, the accuracy of the acquired blood vessel shape varies depending on the segmentation method. Therefore, the calculation function 155b can calculate the reliability B7 based on the set segmentation method.

[0118] Examples of segmentation methods include pixel-wise and model-based. Pixel-wise is a method that classifies each pixel or voxel of an image as to whether or not that pixel or voxel is a blood vessel. Model-based is a method that obtains the 3D shape of blood vessels by preparing a 3D model that represents the rough shape of the blood vessel in advance and deforming the 3D model to fit it to medical images collected from the subject.

[0119] For example, the memory 152 stores a plurality of segmentation methods as shape settings. For example, as shown in FIG. 15, the memory 152 stores two settings, "Pixel-wise" and "Model-based." The setting function 155a selects one of the two settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability B7 based on the analysis condition set by the setting function 155a.

[0120] An example of a method for calculating the phase-related reliability B8 is shown in Figure 16. When acquiring the shape of a blood vessel that is affected by periodic movements such as breathing and pulsation, the accuracy of the acquired blood vessel shape varies depending on the phase of breathing or pulsation of the medical image used. For example, when acquiring the shape of a coronary artery, since there is less movement during the diastole of the heart than during the systole, a more accurate blood vessel shape can be acquired by using an image during the diastole. Therefore, the calculation function 155b can calculate the reliability B8 based on the phase at the time of acquisition of the medical image used to acquire the blood vessel shape.

[0121] The degree of influence of movements such as breathing and pulsation varies depending on the type of medical image. Specifically, the shorter the imaging time, the smaller the influence of movements. Furthermore, if correction can be performed by electrocardiogram synchronization or the like, the influence of movements can be reduced. Therefore, the calculation function 155b may calculate the reliability B8 by further considering the type of medical image.

[0122] For example, the memory 152 stores multiple phases of periodic movements such as respiration and pulsation as shape settings. As an example, the memory 152 stores four settings, such as "diastole (CT)," "systole (CT)," "diastole (IVUS)," and "systole (IVUS)," as shown in FIG. 16. The setting function 155a selects one of the four settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability B8 based on the analysis condition set by the setting function 155a.

[0123] Note that the degree of influence of movements such as breathing and pulsation varies depending on the position of the blood vessel. Therefore, the calculation function 155b may calculate the reliability B8 taking into account the position of the blood vessel. For example, when acquiring the shape of the coronary artery, the calculation function 155b may classify the blood vessels as shown in FIG. 11A and calculate the reliability B8 according to the classification.

[0124] FIG. 17 shows an example of a method for calculating the reliability B9 related to the magnitude of motion. The accuracy of the acquired vascular shape changes due to the influence of periodic motions such as breathing and pulsation, or the subject's body movement during imaging. Therefore, the calculation function 155b can calculate the reliability B9 based on the magnitude of motion when collecting medical images used to acquire the vascular shape. Note that the method for acquiring the magnitude of motion is not particularly limited. For example, the magnitude of motion can be acquired by performing imaging over time to acquire multiple frames of medical images and comparing the frames. Furthermore, the magnitude of motion may be set in advance for each type and phase of blood vessel.

[0125] For example, the memory 152 stores a plurality of settings for the magnitude of the movement of blood vessels as settings regarding the shape. For example, as shown in FIG. 17, the memory 152 stores three settings such as "<X1", "≧X1, <X2", and "≧X2". Here, X1 and X2 are predetermined threshold values regarding the magnitude of the movement. Further, the setting function 155a selects any one of the three settings stored in the memory 152 and determines it as an analysis condition. Then, the calculation function 155b calculates the reliability B9 based on the analysis condition determined by the setting function 155a. Note that the calculation function 155b may calculate the reliability B9 for each position. For example, the calculation function 155b may specify the regions of "<X1", "≧X1, <X2", and "≧X2" respectively, and calculate the reliability B9 for each region.

[0126] An example of a method for calculating the reliability B10 regarding artifacts is shown in FIG. 18. For example, when acquiring a blood vessel shape based on a medical image, the medical image may have artifacts. For example, due to the body movement of the subject during imaging, motion artifacts may occur in the medical image. Also, for example, during the imaging of a CT image, artifacts may occur in the CT image due to malfunctions in some detection elements of the detector or malfunctions in the X-ray output at a specific irradiation angle. And due to the occurrence of such artifacts, the accuracy of the acquired blood vessel shape may decrease.

[0127] For example, the memory 152 stores multiple configuration settings, such as the presence or absence and type of artifacts. For example, as shown in FIG. 18 , the memory 152 stores two configuration settings: “absent” and “present.” The setting function 155a selects one of the two configuration settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b then calculates the reliability B10 based on the analysis condition set by the setting function 155a. While the example of assigning reliability based on the presence and type of artifacts has been described, this is not limiting. The reliability may also be calculated by calculating the size of the area occupied by the artifact, and the reliability may decrease as the size increases. For example, the calculation function 155b detects at least one of the type and amount of artifacts and calculates the reliability B10 for the artifact based on the detection results. For example, if a detector fails to detect a signal in a specific view during CT imaging, a streak-like artifact may appear in the CT image. Here, the calculation function 155b may calculate the volume of a streak artifact portion occurring inside the region corresponding to the created vascular shape mesh, and calculate the reliability B10 so that the reliability decreases as the volume of the artifact portion relative to the vascular shape mesh increases. When calculating the reliability for each location on the vascular shape mesh, it is sufficient to calculate the volume of the streak artifact portion in the segment portion of the vascular shape mesh for which the reliability is to be calculated, rather than the volume of the streak artifact portion relative to the entire vascular shape mesh.

[0128] Various artifact correction methods are known. For example, image processing using filters or noise removal using machine learning techniques can be performed for each type of artifact. If artifacts can be removed or reduced through correction, the accuracy of vascular morphology is expected to improve. Hereinafter, artifact correction processing will be referred to as method M51. When method M51 is added, the calculation function 155b may add "+10" to the reliability B10, as shown in FIG. 18 . Furthermore, although there are various artifact correction methods, the degree to which artifacts can be removed or reduced through image processing varies depending on the type of artifact. For example, device-related artifacts, such as the aforementioned streak artifacts and ring artifacts that occur when a specific detector does not output a detection signal in all views, occur in geometric patterns in CT images, making image processing highly accurate. On the other hand, for motion artifacts that occur due to unexpected movements of the subject during imaging, such as when the subject moves in an unexpected direction, has a convulsion, or experiences arrhythmia, it is possible to estimate the position that the subject would have been in if there had been no movement, but this does not necessarily represent the true value, and therefore even if correction is performed, the accuracy of the correction is not perfect.In this way, it is possible to change the amount of points added depending on the type of artifact to be corrected.

[0129] As in the case of the reliability of the calculation conditions, the calculation function 155b calculates the reliability of the shape based on the reliability B1 to B10. The reliability of the shape may be a single value calculated for the entire blood vessel region, a value calculated for each type of blood vessel, or a value calculated for each position (for example, for each pixel).

[0130] Next, the reliability of the attributes based on the reliability C1 to C4 will be described. Note that the attributes are, for example, the physical properties and states of blood vessels and their surrounding tissues. First, a method for calculating the reliability C1 related to the type of image will be described.

[0131] The characteristics of blood vessels can be obtained from medical images such as CT images, MR images, X-ray angiograms, IVUS images, OCT images, and ICE images. The characteristics can also be obtained from information contained in medical records or analytical images such as perfusion images.

[0132] For example, the distribution of calcification and plaque within blood vessels can be obtained based on brightness values ​​in CT images, and a predetermined property can be assigned. For example, a predetermined hardness based on literature values ​​can be assigned to the location of calcification or plaque. Furthermore, when photon counting or dual energy imaging is performed, the distribution of components can be obtained by material decomposition processing, and the property of each location can be estimated.

[0133] It is also possible to evaluate the components of calcification and plaque and assign properties according to the components. For example, IVUS and OCT images can identify lipid-rich areas within plaque and assign properties according to the amount of lipid. IVUS and OCT images can also be used to evaluate tissue anisotropy. Other properties that can be evaluated include volume compressibility, viscosity, friction coefficient, inflammation, and surface roughness.

[0134] Furthermore, MR images can be used to measure scar regions. For example, it is known that scar regions in cardiac muscle are less likely to deform than other normal tissues. Therefore, by identifying scar regions based on MR images, their characteristics can be obtained.

[0135] There may be cases where the characteristics cannot be obtained from medical images. In such cases, literature values ​​can be used as the characteristics, or the characteristics can be estimated based on a statistical model such as Gaussian estimation.

[0136] FIG. 19 shows an example of a method for calculating reliability C1 related to image type. For example, memory 152 stores a plurality of characteristic acquisition methods as characteristic settings. For example, as shown in FIG. 19, memory 152 stores eight settings, including "CT," "Photon counting CT (PCCT)," "MRI," "IVUS," "OCT," "X-ray angiography," "literature value," and "Gaussian estimation method." Furthermore, setting function 155a selects one of the eight settings stored in memory 152 and sets it as an analysis condition. Then, calculation function 155b calculates reliability C1 based on the analysis condition set by setting function 155a.

[0137] Although medical images of the target patient have not been collected, medical images of another patient with a similar condition may be available. In such cases, it may be possible to reuse characteristics based on the medical images of the other patient. However, since the information is ultimately that of another patient, the calculation function 155b may deduct points from the reliability C1 when characteristics based on medical images of the other patient are reused, compared to when characteristics are obtained based on medical images of the target patient.

[0138] Another possible method is to acquire characteristics using multiple medical images. For example, if an IVUS catheter is inserted into the LAD blood vessel and imaging is performed, the characteristics of the LAD may be acquired based on the IVUS images, and the characteristics of other blood vessels may be acquired based on CT images. Furthermore, if a time series of CT images is acquired, the CT image with the smallest movement for each region may be identified, and the characteristics of each region may be acquired from each CT image. For example, the characteristics of the #8 blood vessel shown in FIG. 11A may be acquired from the CT image at the 77% cardiac phase, and the characteristics of the other blood vessels may be acquired from the CT image at the 99% cardiac phase. If such a method is added, the calculation function 155b may add points to the reliability C1.

[0139] An example of a method for calculating reliability C2 related to imaging conditions is shown in FIG. 20. Four examples of medical images, "CT," "MR," "IVUS," and "OCT," are shown in FIG. 20. That is, even for the same type of medical image, the image quality varies depending on the imaging conditions, and the accuracy of the characteristics acquired based on the medical image also varies. Therefore, the calculation function 155b can calculate reliability C2 based on the set imaging conditions.

[0140] For example, in the case of CT images or MR images, imaging conditions related to the calculation of reliability C2 include "device" and "imaging time." Points may also be added when a specific filter R1 is used as the "filter" or when a magnetic field of 8 T or higher is set as the "static magnetic field." Furthermore, compared to standard CT images, photon-counting imaging or dual-energy imaging can provide additional information regarding characteristics. Therefore, for example, points may be added when a "PCCT" detector is used.

[0141] Furthermore, in the case of IVUS images and OCT images, imaging conditions related to the calculation of reliability C2 include "device," "pulling speed," "rotation speed," etc. Note that IVUS images and OCT images can provide more detailed information about the internal characteristics of blood vessels than regular CT images. Taking into account such differences depending on the type of image, for example, the reliability C2 may be calculated higher for IVUS images and OCT images.

[0142] An example of a method for calculating reliability C3 related to blood vessel type is shown in Figure 21. For example, memory 152 stores multiple blood vessel types as settings for properties. Furthermore, setting function 155a selects one of the multiple settings stored in memory 152 and sets it as an analysis condition. Then, calculation function 155b calculates reliability C3 based on the analysis condition set by setting function 155a.

[0143] An example of a method for calculating the reliability C4 related to the phase is shown in Fig. 22. For example, the memory 152 stores multiple phases of periodic movements such as breathing and pulsation as settings for the characteristics. The setting function 155a selects one of the multiple settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability C4 based on the analysis condition set by the setting function 155a.

[0144] Note that acquiring the characteristics of each position of a blood vessel requires that the shape of the blood vessel be acquired. Therefore, it is often more difficult to acquire the characteristics of blood vessels whose shapes are difficult to acquire. Taking such differences into consideration, when calculating reliability C3 and reliability C4, the changes due to differences in blood vessel type and phase may be made larger compared to the cases of, for example, Figures 11B and 16.

[0145] As with the reliability of the calculation conditions and the reliability of the shape, the calculation function 155b calculates the reliability of the attribute based on the reliability C1 to C4. The reliability of the attribute may be a single value calculated for the entire blood vessel region, a value calculated for each type of blood vessel, or a value calculated for each position (for example, for each pixel).

[0146] Next, we will explain the reliability of fluids based on reliability D1 to D4. The term "fluid" refers to fluid information such as the flow velocity and pressure of blood in blood vessels. When fluid information is available in CFD, it can be used as a fluid condition (boundary condition) for the boundary of the calculation domain, allowing for more accurate analysis. Other examples of fluid information include blood viscosity, Reynolds number, hematocrit value, FFR, WSS, blood glucose level, and temperature.

[0147] Fluid information can be obtained from medical images such as IVUS images, OCT images, MR images, PET images, SPECT images, ICE images, X-ray angiography images, CT images using dual-energy or photon-counting detectors, and ultrasound Doppler images. Fluid information can also be obtained from images included in medical records or analytical images such as perfusion images. Fluid information can also be obtained from blood tests of the subject.

[0148] Alternatively, literature values ​​can be used as fluid information, or the fluid information can be estimated based on a statistical model such as Gaussian estimation. Alternatively, fluid information obtained from another patient with a similar condition can be used, or random values ​​can be set. Furthermore, the fluid information can be readjusted based on the analysis results, and the process can be repeated until a reasonable analysis result is obtained.

[0149] FIG. 23 shows an example of a method for calculating the reliability D1 related to the type of image. For example, the memory 152 stores multiple fluid acquisition methods as settings for the fluid. For example, as shown in FIG. 23, the memory 152 stores eight settings, including "Photon counting CT (PCCT)," "MRI," "IVUS," "OCT," "X-ray angiography," "literature value," "Gaussian estimation method," and "blood test." The setting function 155a selects one of the eight settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability D1 based on the analysis condition set by the setting function 155a.

[0150] An example of a method for calculating the reliability D2 related to the imaging conditions is shown in FIG. 24. In FIG. 24, three examples of medical images are shown: "MR," "IVUS," and "OCT." That is, even for the same type of medical image, the image quality changes depending on the imaging conditions, and the accuracy of the fluid acquired based on the medical image also changes. Therefore, the calculation function 155b can calculate the reliability D2 based on the set imaging conditions.

[0151] Note that the most direct way to obtain information about the blood itself, such as viscosity, is through a blood test. In contrast, since Figure 24 is an evaluation based on the assumption that fluid information will be obtained based on an image, the reliability D2 may be calculated to be lower. Alternatively, a column for the test conditions for the blood test may be provided, and the reliability D2 may be calculated according to the test conditions when the blood test is performed.

[0152] An example of a method for calculating reliability C4 related to blood vessel type is shown in Figure 25. For example, memory 152 stores multiple blood vessel types as fluid settings. Furthermore, setting function 155a selects one of the multiple settings stored in memory 152 and sets it as an analysis condition. Then, calculation function 155b calculates reliability D3 based on the analysis condition set by setting function 155a.

[0153] An example of a method for calculating the reliability D4 related to the phase is shown in Figure 26. For example, the memory 152 stores multiple phases of periodic movements such as breathing and pulsation as settings for the characteristics. The setting function 155a selects one of the multiple settings stored in the memory 152 and sets it as an analysis condition. The calculation function 155b then calculates the reliability D4 based on the analysis condition set by the setting function 155a.

[0154] As with the reliability of the calculation conditions, the reliability of the shape, and the reliability of the properties, the calculation function 155b calculates the reliability of the properties based on the reliability D1 to D4. The reliability of the fluid may be a single value calculated for the entire blood vessel region, a value calculated for each type of blood vessel, or a value calculated for each position (for example, for each pixel).

[0155] As described above, the memory 152 stores multiple settings for each of the calculation conditions, shape, properties, and fluid. The setting function 155a selects one of the multiple settings stored in the memory 152 and sets it as the analysis condition. The calculation function 155b calculates the WSS under the analysis conditions set by the setting function 155a. The calculation function 155b also calculates the reliability of the calculation conditions, shape, properties, and fluid, and, based on these various reliabilities, calculates the reliability of the WSS calculated under the analysis conditions set by the setting function 155a.

[0156] For example, the calculation function 155b calculates the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid within a numerical range of 0 to 100, and calculates the average of these various reliabilities as the reliability of the WSS. The calculation function 155b may also calculate a weighted average as the reliability of the WSS. For example, if the reliability of a calculation condition has a greater influence than others, the calculation function 155b assigns a greater weight to the reliability of the calculation condition and calculates an average value as the reliability of the WSS. The reliability of the WSS may be a single value calculated for the entire vascular region, a value calculated for each type of blood vessel, or a value calculated for each position (e.g., for each pixel).

[0157] The reliability calculated by the calculation function 155b is output by the output function 155c and provided to the user. For example, the output function 155c displays the WSS and the reliability associated with the WSS. For example, the output function 155c displays a display image in which a WSS value is assigned to each position on the three-dimensional image, and also displays a numerical value indicating the reliability. Furthermore, for example, the output function 155c displays a display image in which a WSS value is assigned to each position on the three-dimensional image, and also displays a display image in which a reliability value is assigned to each position on the three-dimensional image. Alternatively, the output function 155c may transmit the reliability calculated by the calculation function 155b to an external device such as the medical information display device 140. In this case, the reliability is displayed in the external device and provided to the user.

[0158] Users who receive the WSS and reliability information can use this information to make diagnoses and develop treatment plans, etc. For example, the WSS can be used to non-invasively determine whether coronary artery disease is worsening and whether treatment such as percutaneous coronary intervention (PCI) is necessary.

[0159] However, because the state inside blood vessels cannot be directly observed, users usually cannot determine how reliable the analysis results, or the WSS, are. Furthermore, while the information used for analysis can be collected from medical images, the medical images themselves contain errors due to resolution limitations and artifacts. Furthermore, it is not always possible to collect all the information necessary for analysis, and analysis must often be performed based on literature values ​​and assumptions. Furthermore, the accuracy of analysis varies due to a variety of factors. Even if a WSS is presented, it is difficult to use it as a basis for judgment if its reliability is unknown.

[0160] In contrast, the medical information processing apparatus 150 according to the first embodiment allows a user to refer to the WSS along with its reliability. For example, if the WSS value indicates that the subject's coronary artery disease is worsening and the reliability is high, the user can determine that treatment such as PCI is necessary. On the other hand, if the reliability is low, the user can determine that additional image acquisition, blood tests, etc. are necessary to make an appropriate decision based on a more reliable WSS. In this way, the medical information processing apparatus 150 can make the WSS more user-friendly by calculating the reliability and providing it to the user.

[0161] Although the above description has been given of a case where multiple settings for each of the calculation conditions, shape, properties, and fluid are stored, the memory 152 may store only one setting for some of the calculation conditions, shape, properties, and fluid. For example, calculation conditions such as mesh size, mesh shape, mesh quality, time resolution, and calculation model may be set to fixed values. In other words, the memory 152 may store only one setting for the calculation conditions. In this case, the setting function 155a can read out the setting stored in the memory 152 and set it as the analysis condition. The same applies to the settings for the shape, properties, and fluid.

[0162] Furthermore, although the case where the reliability of the calculation conditions is calculated based on the reliabilities A1 to A5 has been described, the calculation function 155b may calculate the reliability of the calculation conditions by appropriately omitting the use of some of the reliabilities A1 to A5. In other words, the calculation function 155b calculates the reliability of the calculation conditions based on at least one of the reliabilities A1 to A5. The same applies to the reliability of the shape, the reliability of the property, and the reliability of the fluid.

[0163] Although the case where the reliability of the WSS (total reliability) is calculated based on the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid has been described, the calculation function 155b may calculate the reliability of the WSS by appropriately omitting the use of some of the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid. In other words, the calculation function 155b calculates the reliability of the WSS based on at least one of the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid.

[0164] Furthermore, although the case where the reliability of the WSS is calculated based on at least one of the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid has been described, the calculation function 155b may also calculate the reliability of the WSS using other data.

[0165] For example, the memory 152 also stores patient information. Patient information includes various types of information such as the subject's age, height, weight, region, and race. Such patient information can be acquired, for example, from each department system 130. The calculation function 155b also selects at least one of the calculation conditions, shape, properties, and fluid settings and defines them as analysis conditions. The calculation function 155b also calculates reliability based on the defined analysis conditions and the subject's patient information.

[0166] For example, as described above, literature values ​​may be used in calculating reliability. For example, as shown in FIG. 23, when a blood test or image collection has not been performed on a subject, literature values ​​may be used as fluid information. However, the reliability of literature values ​​is not constant but varies for each subject. Specifically, the larger the population to which the subject belongs, the higher the reliability of literature values. For example, the incidence of diseases such as cerebral infarction increases with age. Therefore, the rate at which blood tests and image collection are performed also increases with age. Therefore, it can be said that the reliability of literature values ​​increases as the subject's age increases.

[0167] Therefore, the calculation function 155b may correct the reliability based on the patient information of the subject. For example, as described above, the calculation function 155b first calculates the reliability of the WSS based on at least one of the reliability of the calculation conditions, the reliability of the shape, the reliability of the properties, and the reliability of the fluid. Then, the calculation function 155b corrects the calculated reliability of the WSS based on the patient information of the subject. For example, the calculation function 155b increases or decreases the reliability of the calculated WSS depending on the age of the subject, or multiplies it by a predetermined coefficient.

[0168] (Second embodiment) In the first embodiment described above, a case where both WSS and reliability are calculated and displayed, as shown in FIG. 2, for example, is described. In contrast, in the second embodiment, a case where reliability is calculated first and then WSS is calculated based on the reliability is described. Note that in the second embodiment, only WSS may be displayed, and reliability may not be displayed. Hereinafter, the same reference numerals are used to denote the points described in the first embodiment, and description thereof will be omitted.

[0169] An example of processing according to the second embodiment will be described with reference to Fig. 27. Fig. 27 is a flowchart showing the processing procedure of processing performed by each processing function of the processing circuitry 155 of the medical image processing apparatus 150 according to the second embodiment.

[0170] First, the calculation function 155b acquires various data to be used for analysis (step S201). For example, the calculation function 155b acquires a coronary artery CT image of the subject from the X-ray CT device 110 or the medical image storage device 120 via the NW interface 151.

[0171] Next, the calculation function 155b determines whether or not there are other analysis results (step S202). Here, the other analysis results are analysis results related to the subject, and are analysis results different from the WSS calculated in step S208, which will be described later. For example, if an analysis has been performed on the subject in the past, the calculation function 155b determines that there are other analysis results (Yes in step S202). The other analysis results are stored, for example, in the medical image storage device 120 or each department system 130. In addition, the calculation function 155b acquires the other analysis results via the NW interface 151 and sets an area not to be calculated (step S203).

[0172] For example, WSS is used to determine whether or not the perfusion function of the coronary artery is impaired. Therefore, there is little need to calculate WSS for regions where it is clear that the perfusion function of the coronary artery is not impaired. Therefore, the calculation function 155b acquires, for example, the results of a CT perfusion performed in the past as another analysis result, and excludes regions where the perfusion function is not impaired from the calculation target. Specifically, the calculation function 155b acquires the distribution of the amount of blood (or the amount of contrast agent) supplied to the myocardium based on the perfusion image, and excludes regions where the amount exceeds a threshold value from the calculation target, assuming that there is no disease.

[0173] Alternatively, the calculation function 155b may set a region to be calculated in step S203. For example, the calculation function 155b sets only a region likely to have a disease as a region to be calculated. For example, the calculation function 155b acquires a region with calcification or plaque identified based on previously collected medical images as another analysis result, and sets the region as a region to be calculated. Also, for example, the calculation function 155b sets only a region likely to have a high WSS as a region to be calculated. For example, the calculation function 155b acquires blood flow velocity, blood vessel diameter, change in blood vessel diameter, etc. as another analysis result. Then, the calculation function 155b sets a region with a high blood flow velocity, a region with narrow blood vessels, a region with a large change in blood vessel diameter, etc. as a region to be calculated. Note that if there are no other analysis results (No in step S202), step S203 is omitted.

[0174] Next, the setting function 155a sets the analysis conditions (step S204). For example, the memory 152 stores a plurality of settings for at least one of calculation conditions, shape, properties, and fluid, and the setting function 155a selects one of the settings stored in the memory 152 and sets it as the analysis condition.

[0175] Next, the calculation function 155b calculates the reliability of the WSS calculated under the analysis conditions set by the setting function 155a (step S205). Note that, as long as the analysis conditions are set, the calculation function 155b can calculate the reliability of the WSS calculated under those analysis conditions even if the WSS has not actually been calculated. However, some items, such as reliability B6 related to intermediate calculation results, can be calculated only after the WSS has been calculated. The calculation function 155b may omit such items as appropriate when calculating the WSS. Furthermore, if an area not to be calculated is set in step S203, the calculation function 155b can omit calculating the reliability for that area.

[0176] Next, the calculation function 155b determines whether the calculated reliability is equal to or greater than a threshold (step S206). If the reliability is lower than the threshold (No in step S206), the calculation function 155b sets regions not to be included in the calculation (step S207). That is, since the use of calculating a WSS with low reliability is limited, the calculation function 155b excludes regions with low reliability from the WSS calculation targets. For example, the calculation function 155b calculates the reliability for each of the three major coronary arteries, and sets only blood vessels with reliability above the threshold as target regions for calculating the WSS. Note that if the reliability of all blood vessels exceeds the threshold (Yes in step S206), step S207 is omitted.

[0177] Next, the calculation function 155b executes the analysis under the analysis conditions set by the setting function 155a and calculates the WSS (step S208). Note that if an area not to be calculated is set in step S203 or step S207, the calculation function 155b can omit the calculation of the WSS for that area. Then, the output function 155c displays the calculated WSS and reliability on the display 154 (step S209).

[0178] Another example of the processing according to the second embodiment will be described with reference to Fig. 28. Fig. 28 is a flowchart showing the processing procedure of processing performed by each processing function of the processing circuitry 155 of the medical image processing apparatus 150 according to the second embodiment.

[0179] First, the calculation function 155b acquires various data to be used in the analysis (step S301). For example, the calculation function 155b acquires a coronary artery CT image of the subject from the X-ray CT device 110 or the medical image storage device 120 via the NW interface 151. Next, the calculation function 155b determines whether or not there are other analysis results (step S302). If there are other analysis results (Yes in step S302), the calculation function 155b sets regions not to be used for calculation based on the other analysis results (step S303). Note that if there are no other analysis results (No in step S302), step S303 is omitted. Next, the setting function 155a sets analysis conditions (step S304). Furthermore, the calculation function 155b calculates the reliability of the WSS calculated under the analysis conditions set by the setting function 155a (step S305).

[0180] Next, the setting function 155a determines whether the calculated reliability is equal to or greater than a threshold (step S306). If the reliability is lower than the threshold (No at step S306), the setting function 155a resets the analysis conditions (step S307). Specifically, the setting function 155a resets the analysis conditions so as to increase the reliability.

[0181] For example, when the shape or the like is acquired based on a CT image, the setting function 155a can increase the calculated reliability by changing the setting to a higher-dose CT image or a different type of medical image such as an IVUS image. Here, the output function 155c may suggest to the user that a higher-dose CT image or a different type of medical image be acquired, or may issue an examination order.

[0182] Furthermore, for example, the setting function 155a can increase the reliability of the calculation by making the mesh finer, changing the shape of the mesh, changing the mesh quality from a primary mesh to a secondary mesh, changing to a more complex calculation model, etc. In this way, by sequentially adjusting the settings according to the reliability, it is possible to shorten the calculation time while ensuring the required accuracy.

[0183] Note that resetting the analysis conditions to increase reliability often increases the amount of calculation required for analysis. For example, when selecting a calculation model, there is usually a trade-off between calculation accuracy and calculation time. Furthermore, from the perspective of imaging time, radiation exposure, and the like, collecting medical images over a wide area places a heavy burden on the subject. Therefore, the setting function 155a may reset the analysis conditions to increase reliability only for some regions. For example, the setting function 155a may reset the analysis conditions only for specific blood vessels or regions, such as blood vessels that cause severe symptoms when stenosis occurs or regions with a high number of cases.

[0184] After the analysis conditions are reset, the calculation function 155b recalculates the reliability of the WSS calculated under the reset analysis conditions. If the calculated reliability exceeds the threshold (Yes in step S306), the calculation function 155b calculates the WSS under the analysis conditions last set (step S308). If an area not to be calculated is set in step S303, the calculation function 155b can omit the calculation of the WSS for that area. Then, the output function 155c displays the calculated WSS and reliability on the display 154 (step S309).

[0185] In addition, in steps S209 and S309, the output function 155c may transmit the reliability calculated by the calculation function 155b to an external device such as the medical information display device 140. In this case, the reliability is displayed in the external device and provided to the user. Also, some of the processes shown in Figure 27, such as steps S202 and S203, can be omitted as appropriate. Similarly, some of the processes shown in Figure 28, such as steps S302 and S303, can be omitted as appropriate.

[0186] Furthermore, in steps S209 and S309, the output function 155c may omit displaying the reliability and display only the WSS. In the case shown in FIG. 27, the WSS is calculated only for regions where the reliability exceeds the threshold value through the processes of steps S206 and S207. In the case shown in FIG. 28, the analysis conditions are reset through the processes of steps S306 and S307 so that the reliability exceeds the threshold value. That is, the reliability of the WSS calculated in the cases shown in FIGS. 27 and 28 is guaranteed. Therefore, the user can use the WSS with confidence even if the reliability is not displayed. That is, the medical image processing apparatus 150 according to the second embodiment can make the WSS easier for the user to use, regardless of whether the reliability is displayed.

[0187] (Other embodiments) In the above-described embodiment, an example in which the WSS and reliability are calculated by the medical image processing device 150 has been described, but the embodiment is not limited to this. For example, a server-client type computer configuration may be used in which some calculations are performed on the server side. For example, computationally intensive processes such as fluid simulation for calculating the WSS may be performed on the server side, and other processes may be performed on the client side.

[0188] In addition, by setting the same value for each structure of blood vessels and transmitting the data associated with coordinates, data size can be significantly reduced through data compression. For example, if there are two types of structures, vascular wall and non-vascular wall, the structures can be expressed using two values, 0 or 1. Similarly, if there are 10 types of structures, the structures can be expressed using 10 values ​​from 0 to 9. By communicating only the data necessary for calculation between the server and client in this way, transfer speed and calculation costs on the server side can be reduced. Furthermore, the server may be configured to specify the vascular branches to be calculated based on user instructions and calculate the WSS for only those vascular branches.

[0189] In the above-described embodiment, WSS has been described as an index value related to blood flow, but other index values ​​can also be similarly applied. For example, the calculation function 155b can calculate the reliability of other index values, such as FFR (Fractional Flow Reserve, coronary blood flow cerebral reserve ratio), iFR (Instantaneous wave-Free Ratio), blood flow rate, blood pressure, changes over time of these index values, or index values ​​combining these index values, index values ​​combining index values ​​related to blood flow with index values ​​of the geometric shape of blood vessels, and index values ​​obtained by combining index values ​​related to blood flow with functional indexes of target tissues that supply blood flow, and the above-described embodiment can also be applied to these index values.

[0190] In the above embodiment, the index value related to blood flow has been described as being calculated by the medical information processing device 150, but the index value related to blood flow may be calculated by another device. For example, the output function 155c can acquire the WSS calculated by another device via the NW interface 151, associate it with the reliability calculated by the calculation function 155b, and display it on the display 154.

[0191] In the above-described embodiment, the setting unit, the calculation unit, and the output unit in this specification are respectively realized by the setting function, the calculation function, and the output function of a processing circuit, but the embodiment is not limited to this. For example, the setting unit, the calculation unit, and the output unit in this specification may be realized by only hardware, only software, or a combination of hardware and software, in addition to being realized by the setting function, the calculation function, and the output function described in the embodiment.

[0192] The term "processor" used in the above description refers to a circuit such as a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), or a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), or a field programmable gate array (FPGA)). When the processor is a CPU, for example, the processor realizes its function by reading and executing a program stored in a memory circuit. On the other hand, when the processor is an ASIC, for example, instead of storing a program in a memory circuit, the function is directly incorporated into the processor circuit as a logic circuit. Note that each processor in the embodiments is not limited to being configured as a single circuit, but may be configured as a single processor by combining multiple independent circuits to realize its function. Furthermore, multiple components in each figure may be integrated into a single processor to realize its function.

[0193] 1, a single memory 152 is described as storing programs corresponding to each processing function of the processing circuit 155, but the embodiment is not limited to this. For example, a configuration may be adopted in which multiple memories 152 are distributed and the processing circuit 155 reads corresponding programs from individual memories 152. Also, instead of storing programs in the memory 152, a configuration may be adopted in which the programs are directly embedded in the circuitry of the processor. In this case, the processor realizes the functions by reading and executing the programs embedded in the circuitry.

[0194] For example, a program executed by a processor may be provided in advance in a read-only memory (ROM) or a storage circuit. The program may also be provided in a format installable or executable by these devices, recorded on a non-transitory computer-readable storage medium such as a compact disk (CD)-ROM, a flexible disk (FD), a recordable CD-R, or a digital versatile disk (DVD). The program may also be stored on a computer connected to a network such as the Internet and provided or distributed by downloading it via the network. For example, the program may be composed of modules including the above-described processing functions. In actual hardware, a CPU reads and executes the program from a storage medium such as a ROM, whereby each module is loaded into a main memory device and generated on the main memory device.

[0195] The components of each device according to the above-described embodiments are conceptual and functionally independent, and are not necessarily physically configured as shown in the drawings. In other words, the specific form of distribution and integration of each device is not limited to that shown in the drawings, and all or part of each device can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0196] Furthermore, among the processes described in the above-mentioned embodiments and modifications, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method.In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0197] According to at least one of the embodiments described above, index values ​​relating to blood flow can be made easier to use.

[0198] 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]

[0199] 100 Medical Information Processing System 110 X-ray CT device 120 Medical image storage device 130 Departmental Systems 140 Medical information display device 150 Medical information processing device 151 Network Interface 152 memory 153 Input Interface 154 Display 155 Processing Circuit 155a Setting Function 155b Calculation Function 155c output function

Claims

1. a storage unit that stores a plurality of settings for at least one of calculation conditions, shape, properties, and fluid; a setting unit that selects at least one of the plurality of settings and determines it as an analysis condition; a calculation unit that calculates at least one of a first reliability regarding calculation conditions, a second reliability regarding shape, a third reliability regarding properties, and a fourth reliability regarding fluid based on the analysis conditions, and calculates a reliability of an index value regarding blood flow calculated under the analysis conditions based on at least one of the first reliability, the second reliability, the third reliability, and the fourth reliability; A medical information processing device comprising:

2. The medical image processing apparatus according to claim 1 , wherein the calculation unit further calculates the index value under the analysis conditions.

3. The medical information processing apparatus according to claim 1 , further comprising an output unit that outputs the reliability.

4. The medical image processing apparatus according to claim 3 , wherein the output unit displays the reliability in association with the index value.

5. The medical image processing apparatus according to claim 1, wherein the calculation unit calculates the index value for a target region set based on the reliability.

6. 5. The medical image processing device according to claim 1, wherein the calculation unit determines a target region based on an analysis result different from the index value, and calculates the index value for the target region.

7. the setting unit redefines the analysis conditions based on the reliability; 7. The medical image processing apparatus according to claim 1, wherein the calculation unit calculates the index value under the analysis conditions that have been reset by the setting unit.

8. The medical image processing apparatus according to claim 7 , wherein the setting unit redefines the analysis conditions so as to increase the reliability.

9. The medical information processing device according to any one of claims 1 to 8, wherein the calculation unit calculates the first reliability based on at least one of reliability related to mesh size, reliability related to mesh shape, reliability related to mesh quality, reliability related to time resolution, and reliability related to a computational model.

10. 10. The medical information processing device according to claim 9, wherein the calculation unit detects areas where turbulence is likely to occur based on at least one of blood vessel shape and blood fluid information, and calculates at least one of a reliability related to mesh size, a reliability related to mesh shape, and a reliability related to mesh quality based on the detection result.

11. 10. The medical information processing device according to claim 9, wherein the calculation unit detects areas of large movement based on time-series medical images, and calculates at least one of a reliability related to mesh size, a reliability related to mesh shape, and a reliability related to mesh quality based on the detection result.

12. The medical image processing apparatus according to claim 9 , wherein the calculation unit calculates a reliability of the calculation model based on a type of boundary condition.

13. The medical image processing apparatus according to claim 12 , wherein the types of boundary conditions include types of conditions obtained by further performing calculations based on information obtained from an image of the subject.

14. The medical information processing device according to any one of claims 1 to 13, wherein the calculation unit calculates the second reliability based on at least one of reliability related to image type, reliability related to imaging conditions, reliability related to blood vessel type, reliability related to blood vessel shape, reliability related to intravascular structures, reliability related to artifacts, reliability related to intermediate calculation results, reliability related to shape acquisition method, reliability related to phase, and reliability related to magnitude of movement.

15. The medical image processing device according to claim 14 , wherein the calculation unit detects the position and type of an intravascular structure, and calculates the reliability of the intravascular structure for each position according to the type of the intravascular structure.

16. The medical image processing apparatus according to claim 14 , wherein the calculation unit detects at least one of a type of an artifact and an amount of the artifact, and calculates a reliability of the artifact based on a detection result.

17. The medical information processing device according to any one of claims 1 to 16, wherein the calculation unit calculates the third reliability based on at least one of reliability related to image type, reliability related to imaging conditions, reliability related to blood vessel type, and reliability related to phase.

18. The medical information processing device according to any one of claims 1 to 17, wherein the calculation unit calculates the fourth reliability based on at least one of reliability related to image type, reliability related to imaging conditions, reliability related to blood vessel type, and reliability related to phase.

19. The storage unit further stores patient information, The medical information processing apparatus according to claim 1, wherein the calculation unit calculates the reliability based on the analysis conditions and the patient information.

20. a storage unit that stores a plurality of settings for at least one of calculation conditions, shape, properties, and fluid; a setting unit that selects at least one of the plurality of settings and determines it as an analysis condition; a calculation unit that calculates at least one of a first reliability regarding calculation conditions, a second reliability regarding shape, a third reliability regarding properties, and a fourth reliability regarding fluid based on the analysis conditions, and calculates a reliability of an index value regarding blood flow calculated under the analysis conditions based on at least one of the first reliability, the second reliability, the third reliability, and the fourth reliability; A medical information processing system comprising:

21. Select at least one of a plurality of settings regarding at least one of calculation conditions, shape, properties, and fluid, and define it as an analysis condition; Based on the analysis conditions, at least one of a first reliability regarding calculation conditions, a second reliability regarding shape, a third reliability regarding properties, and a fourth reliability regarding fluid is calculated, and based on at least one of the first reliability, the second reliability, the third reliability, and the fourth reliability, a reliability is calculated for an index value regarding blood flow calculated under the analysis conditions. A medical information processing method, comprising:

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