Medical image processing device, medical image processing method and program

Abstract

To provide more useful information about fluid information calculated based on medical image data.SOLUTION: A medical image processing device according to an embodiment comprises: an acquisition unit; a calculation unit; and an evaluation unit. The acquisition unit acquires first medical image data acquired based on the first imaging method and second medical image data acquired based on the second imaging method different from the first imaging method in a prescribed time phase. The calculation unit calculates first fluid information based on the first medical image data and second fluid information based on the second medical image data. The evaluation unit evaluates a region based on the prescribed time phase or at least one of the first medical image data and the second medical image data on the basis of the first fluid information and the second fluid information in the prescribed time phase.SELECTED DRAWING: Figure 6

Description

[Technical field]

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

[0002] There is known a technique for calculating fluid information based on medical image data. For example, fluid information such as blood flow rate in blood vessels or the heart can be calculated by blood flow simulation using X-ray CT (Computed Tomography) images or MRI (Magnetic Resonance Imaging) images, or the Doppler method in ultrasound images. Here, even when the same subject is the subject, different fluid information may be calculated if the imaging method of the medical image data used to calculate the fluid information is different. For example, the fluid information calculated based on the X-ray CT image may differ from the fluid information calculated based on the ultrasound image. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-131645 A [Patent Document 2] JP 2014-188323 A [Patent Document 3] JP 2018-94020 A [Non-patent literature]

[0004] [Non-Patent Document 1] Creigen, Vincent, et al. "Modeling a heart pump" European Study Group Mathematics with Industry 7 (2007) [Non-Patent Document 2] Ursino, Mauro. "Interaction between carotid baroregulation and the pulsating heart: a mathematical model." American Journal of Physiology-Heart and Circulatory Physiology 275.5 (1998): H1733-H1747 Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to provide more useful information regarding fluid information calculated based on medical image data. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0006] A medical image processing device according to an embodiment includes an acquisition unit, a calculation unit, and an evaluation unit. The acquisition unit acquires first medical image data acquired based on a first imaging technique and second medical image data acquired based on a second imaging technique different from the first imaging technique at a predetermined time phase. The calculation unit calculates first fluid information based on the first medical image data and second fluid information based on the second medical image data. The evaluation unit evaluates the predetermined time phase or a region based on at least one of the first medical image data and the second medical image data based on the first fluid information and the second fluid information at the predetermined time phase. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a medical image processing system according to the first embodiment. [Diagram 2]FIG. 2 is a flowchart showing an example of processing by the processing circuitry of the medical image processing apparatus according to the first embodiment. [Diagram 3] FIG. 3 is a diagram for explaining a method for calculating fluid information according to the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining a method for calculating fluid information according to the first embodiment. [Diagram 5] FIG. 5 is a flowchart showing the details of step S4 in FIG. [Figure 6] FIG. 6 is a diagram showing an example of calculation of blood flow volume and degree of coincidence according to the first embodiment. [Figure 7] FIG. 7 is a diagram showing a display example according to the first embodiment. [Figure 8] FIG. 8 is a diagram showing a display example according to the first embodiment. [Figure 9] FIG. 9 is a diagram showing a display example according to the second embodiment. [Figure 10] FIG. 10 is a diagram showing a display example according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, embodiments of a medical image processing apparatus, a medical image processing method, and a program will be described in detail with reference to the accompanying drawings.

[0009] (First embodiment) In this embodiment, a medical image processing system 1 including a medical image processing device 20 will be described as an example. For example, as shown in Fig. 1, the medical image processing system 1 has a medical image diagnostic device 10, a medical image processing device 20, and an image storage device 30. Fig. 1 is a block diagram showing an example of the configuration of the medical image processing system 1 according to the first embodiment. The medical image diagnostic device 10, the medical image processing device 20, and the image storage device 30 are connected to each other via a network NW.

[0010] As long as they can be connected via the network NW, the locations where the devices included in the medical image processing system 1 are installed are arbitrary. For example, the image storage device 30 may be installed in a hospital different from the hospital where the medical image diagnostic device 10 and the medical image processing device 20 are installed, or in another facility. That is, the network NW may be configured as a closed local network within the facility, or may be a network via the Internet.

[0011] The medical image diagnostic device 10 is a device that captures an image of a subject and collects medical image data. The various data handled in this specification are typically digital data. The medical image diagnostic device 10 is, for example, a medical modality such as an X-ray CT device, an MRI device, an ultrasound diagnostic device, an X-ray diagnostic device, a SPECT (Single Photon Emission Computed Tomography) device, or a PET (Positron Emission computed Tomography) device. Although a single medical image diagnostic device 10 is shown in FIG. 1, the medical image processing system 1 may include a plurality of medical image diagnostic devices 10. The medical image processing system 1 may also include a plurality of types of medical image diagnostic devices 10. For example, the medical image processing system 1 may include an X-ray CT device and an ultrasound diagnostic device as the medical image diagnostic device 10.

[0012] The image storage device 30 is an image database that stores medical image data collected by the medical image diagnostic device 10. For example, the image storage device 30 includes an arbitrary storage device inside or outside the device, and manages the medical image data acquired from the medical image diagnostic device 10 via the network NW in the form of a database. For example, the image storage device 30 is a PACS (Picture Archiving and Communication System) server. The image storage device 30 may also be realized by a group of servers (cloud) connected to the medical image processing system 1 via the network NW.

[0013] The medical image processing device 20 is a device that acquires medical image data collected by the medical image diagnostic device 10 and performs various processes described below. For example, as shown in Fig. 1, the medical image processing device 20 includes a memory 21, a display 22, an input interface 23, and a processing circuit 24.

[0014] The memory 21 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, a hard disk, an optical disk, etc. For example, the memory 21 stores medical image data. The memory 21 also stores a program for enabling the circuitry included in the medical image processing device 20 to realize its functions.

[0015] The display 22 is, for example, a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 22 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the main body of the medical image processing device 20. Control of the display on the display 22 will be described later.

[0016] The input interface 23 receives various input operations from the user, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 24. For example, the input interface 23 is realized by a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touchpad that performs input operations by touching the operation surface, a touch screen in which a display screen and a touchpad are integrated, a non-contact input circuit using an optical sensor, a voice input circuit, and the like. The input interface 23 may be configured as a tablet terminal or the like capable of wireless communication with the main body of the medical image processing device 20. The input interface 23 may also be a circuit that receives input operations from the user by motion capture. For example, the input interface 23 can receive the user's body movement, 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 23 is not limited to only those that include physical operation parts such as a mouse and a keyboard. For example, an example of the input interface 23 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the medical image processing device 20 and outputs this electrical signal to the processing circuit 24.

[0017] The processing circuitry 24 controls the operation of the entire medical image processing device 20 by executing a control function 24a, an acquisition function 24b, a calculation function 24c, an evaluation function 24d, and an output function 24e. The acquisition function 24b is an example of an acquisition unit. The calculation function 24c is an example of a calculation unit. The evaluation function 24d is an example of an evaluation unit. The output function 24e is an example of an output unit.

[0018] For example, the processing circuit 24 reads out a program corresponding to the control function 24a from the memory 21 and executes it, thereby controlling various functions such as the acquisition function 24b, the calculation function 24c, the evaluation function 24d, and the output function 24e based on various input operations received from a user via the input interface 23.

[0019] The processing circuitry 24 also acquires medical image data relating to the subject by reading from the memory 21 a program corresponding to the acquisition function 24b and executing it. The processing circuitry 24 also calculates fluid information based on the medical image data by reading from the memory 21 a program corresponding to the calculation function 24c and executing it. The processing circuitry 24 also performs evaluation based on the fluid information by reading from the memory 21 a program corresponding to the evaluation function 24d and executing it. The processing circuitry 24 also reads from the memory 21 a program corresponding to the output function 24e and executes it to output various information based on the results of evaluation by the evaluation function 24d. Details of the processing by the acquisition function 24b, the calculation function 24c, the evaluation function 24d, and the output function 24e will be described later.

[0020] 1, each processing function is stored in the form of a program executable by a computer in the memory 21. The processing circuitry 24 is a processor that realizes a function corresponding to each program by reading and executing the program from the memory 21. In other words, the processing circuitry 24 in a state in which a program has been read has a function corresponding to the read program.

[0021] 1, the control function 24a, the acquisition function 24b, the calculation function 24c, the evaluation function 24d, and the output function 24e are realized by a single processing circuit 24. However, the processing circuit 24 may be configured by combining a plurality of independent processors, and each processor may execute a program to realize the functions. Furthermore, each processing function of the processing circuit 24 may be realized by being appropriately distributed or integrated in a single or a plurality of processing circuits.

[0022] The processing circuitry 24 may also realize functions by using a processor of an external device connected via a network NW. For example, the processing circuitry 24 reads out and executes a program corresponding to each function from the memory 21, and realizes each function shown in FIG. 1 by using a server group (cloud) connected to the medical image processing device 20 via the network NW as a computational resource.

[0023] The above describes an example of the configuration of the medical image processing system 1 including the medical image processing device 20. With this configuration, the processing circuitry 24 in the medical image processing device 20 makes it possible to provide more useful information regarding fluid information calculated based on medical image data.

[0024] The processing performed by the processing circuitry 24 will be described below with reference to the flowchart in Fig. 2. Fig. 2 is a flowchart showing an example of processing performed by the processing circuitry 24 of the medical image processing apparatus 20 according to the first embodiment.

[0025] First, the acquisition function 24b acquires medical image data related to a subject (step S1). Specifically, the acquisition function 24b acquires first medical image data acquired based on a first imaging technique and second medical image data acquired based on a second imaging technique different from the first imaging technique.

[0026] Here, as an example, a case will be described in which an X-ray CT image I1 is acquired as the first medical image data, and an ultrasound image I2 is acquired as the second medical image data. In this case, the medical image processing system 1 includes at least an X-ray CT device and an ultrasound diagnostic device as the medical image diagnostic device 10. The X-ray CT device collects the X-ray CT image I1 from a subject. Also, the ultrasound diagnostic device collects the ultrasound image I2 from the subject.

[0027] The X-ray CT image I1 and the ultrasound image I2 are collected at a predetermined time phase by the X-ray CT device and the ultrasound diagnostic device. Here, the predetermined time phase is a predetermined time point or a predetermined phase in heartbeat, respiration, etc. For example, the X-ray CT image I1 and the ultrasound image I2 are collected at the same time point by simultaneously capturing images of the same subject. That is, the X-ray CT image I1 and the ultrasound image I2 may be images captured at the same time (on the same date and time). Also, for example, the X-ray CT image I1 and the ultrasound image I2 are collected by identifying the same phase in the periodic movement of the subject by electrocardiogram synchronization, heart sound synchronization, respiration synchronization, etc., and capturing each image at the phase. That is, the X-ray CT image I1 and the ultrasound image I2 do not have to be images captured at the same time as long as they are captured so that the phases in heartbeat, respiration, etc. match.

[0028] The acquisition function 24b acquires an X-ray CT image I1 and an ultrasound image I2 from the X-ray CT device and the ultrasound diagnostic device. The acquisition function 24b may acquire various medical image data directly from the X-ray CT device and the ultrasound diagnostic device, or may acquire the medical image data via another device such as an image storage device 30.

[0029] Here, the acquisition function 24b acquires the X-ray CT image I1 and the ultrasound image I2 at a predetermined time phase. For example, the acquisition function 24b acquires the X-ray CT image I1 and the ultrasound image I2 collected at the same time, or acquires the X-ray CT image I1 and the ultrasound image I2 captured at the same phase by electrocardiogram synchronization or the like. Here, the X-ray CT image I1 and the ultrasound image I2 may be time-series images captured at multiple time phases. In this case, the acquisition function 24b acquires the X-ray CT image I1 and the ultrasound image I2 so that they have at least one time phase in common.

[0030] In the following, as an example, a case will be described in which various medical image data are collected with the mitral valve of a subject as an imaging target, and fluid information is calculated regarding the blood flow through the mitral valve. For example, the acquisition function 24b acquires a 4D-CT image of the mitral valve as an X-ray CT image I1. That is, the acquisition function 24b acquires a plurality of 3D data captured in a plurality of time phases as the X-ray CT image I1. For example, the acquisition function 24b acquires a 4D-CT image including 3D data for six time phases captured in six time phases T1 to T6. Each of these time phases T1 to T6 is associated with the movement of the subject's heart by, for example, electrocardiogram synchronization or heart sound synchronization. Such a correspondence can be stored, for example, as supplementary information of the X-ray CT image I1 (such as a DICOM (Digital Imaging and Communications in Medicine) header) or stored in the memory 21. Similarly, the acquisition function 24b acquires an ultrasound image I2. The ultrasound images I2 are acquired, for example, at some or all of the time phases T1 to T6 described above.

[0031] As an example, the acquisition function 24b acquires medical image data when an instruction received from a user via the input interface 23 is used as a trigger. Alternatively, the acquisition function 24b may automatically acquire medical image data newly collected by the medical image diagnostic apparatus 10. For example, the acquisition function 24b may monitor the image storage device 30, and when new medical image data is stored in the image storage device 30, acquire the medical image data.

[0032] The acquisition function 24b may also acquire newly collected medical image data when the medical image data satisfies a predetermined condition. The predetermined condition may be any condition that can determine the state of the medical image data. For example, the acquisition function 24b may set the imaging protocol as the predetermined condition, and acquire the medical image data when the medical image data is imaged with an imaging protocol that targets the heart. For example, the acquisition function 24b may set the reconstruction method as the predetermined condition, and acquire the medical image data when the medical image data is enlarged and reconstructed. The acquisition function 24b may also combine a plurality of conditions, such as an imaging protocol and a reconstruction method, to set the predetermined condition, and acquire the medical image data when the predetermined condition is satisfied.

[0033] In addition, when patient information other than medical image data is used in step S2 and subsequent steps, the acquisition function 24b can further acquire the patient information. For example, in the calculation of fluid information in step S2 and the like, patient information such as blood pressure, weight, blood test values, etc. may be used as calculation parameters. In such a case, the acquisition function 24b can acquire necessary patient information from the subject's chart managed by the electronic chart system, the hospital information system (HIS), the radiology information system (RIS), etc.

[0034] Next, the calculation function 24c calculates fluid information based on the X-ray CT image I1 (step S2), and also calculates fluid information based on the ultrasound image I2 (step S3). The order of steps S2 and S3 is arbitrary, and they may be performed in parallel.

[0035] First, step S2 will be described. For example, the calculation function 24c calculates the blood flow passing through the mitral valve based on the X-ray CT image I1. Hereinafter, the blood flow calculated based on the X-ray CT image I1 will also be referred to as the blood flow R1.

[0036] For example, the calculation function 24c can numerically obtain target fluid information by forming simultaneous equations such as the Navies-Stokes equation, the continuity equation, the Maxwell equation, and the state equation, and inputting various parameters to the equations. Also, for example, the calculation function 24c may design an electric circuit model that simulates the circulatory dynamics of a living body in advance based on the Windkessel model, the pulse wave propagation model, etc., and obtain target fluid information by inputting morphological information of a target organ and patient information to the electric circuit model. An example of the electric circuit model is shown in FIG. 3. FIG. 3 is a diagram for explaining a method for calculating fluid information according to the first embodiment.

[0037] First, the calculation function 24c designs an electric circuit model of FIG. 3 that simulates the circulatory dynamics of a living body, and stores it in the memory 21. For example, the blood flow rate can be simulated as a current, and the force pushing out the blood (blood pressure) can be simulated as a voltage. Also, the electric resistance at each position can be set based on blood test values ​​such as the hematocrit value. The electric circuit model can be constructed based on the methods shown in, for example, Non-Patent Document 1 and Non-Patent Document 2.

[0038] For example, the calculation function 24c acquires parameters such as the subject's weight, systolic blood pressure, diastolic blood pressure, and hematocrit value from the DB and inputs them to the electric circuit model, as shown in Fig. 4. Fig. 4 is a diagram for explaining a method for calculating fluid information according to the first embodiment. The DB in Fig. 4 is various databases such as the image storage device 30, the electronic medical record system, the RIS, and the HIS.

[0039] The calculation function 24c also identifies the regions of the left atrium and the left ventricle based on the X-ray CT image I1 acquired from the DB, and calculates the volume of each region. The calculation function 24c also identifies the region of the mitral valve based on the X-ray CT image I1, and calculates the valve orifice area. For example, the calculation function 24c acquires coordinate information of each pixel in the X-ray CT image I1, which indicates each region, such as the left atrium, the left ventricle, and the mitral valve. As a specific process, the calculation function 24c can identify each region by manually accepting the designation of the position of each region via the input interface 23. Alternatively, the calculation function 24c may identify each region based on the anatomical structure depicted in the X-ray CT image I1 using a known region extraction technique. Examples of known region extraction techniques include, for example, Otsu's binarization method based on CT values, a region expansion method, a snake method, a graph cut method, and a mean shift method. Note that the above-mentioned method is merely an example, and this process may be any method as long as it can identify each region from the X-ray CT image I1. For example, each region may be identified from the X-ray CT image I1 by machine learning technology (including deep learning). For example, a trained model that is functional to identify the region of the mitral valve from the input medical image data can be constructed by learning a neural network using medical image data collected about the mitral valve as input data and a region of the mitral valve identified by a radiologist or the like in the medical image data as output data. The calculation function 24c can also identify each region using such a trained model. Next, the calculation function 24c calculates the volumes of the left atrium and left ventricle and the valve orifice area of ​​the mitral valve from each identified region. These volumes and areas can be calculated, for example, from the number of pixels indicated by each region.

[0040] The calculation function 24c can calculate the blood flow rate R1 passing through the mitral valve by inputting various parameters such as the subject's weight and the left atrial volume into the electric circuit model. Note that the calculation function 24c may use literature values ​​or known experimental values ​​as appropriate for parameters that cannot be acquired from the DB in FIG.

[0041] The calculation process of the blood flow rate R1 in step S2 is performed, for example, for each of the X-ray CT images I1 of the time phases T1 to T6. In this case, the blood flow rate R1 corresponding to each of the X-ray CT images I1 of the time phases T1 to T6 is calculated. Alternatively, the calculation process of the blood flow rate R1 in step S2 may be performed based on one time phase, and then the blood flow rate R1 of the other time phases may be estimated. For example, the calculation function 24c may obtain morphological information such as the valve orifice area from the X-ray CT image I1 of the time phase T1 to calculate the blood flow rate R1, and estimate the blood flow rate R1 of the other time phases based on the calculation result. For example, fluid structure analysis (FSI analysis) may be used for the estimation. The calculation function 24c may calculate the blood flow rate R1 for only a part of the time phases T1 to T6. The calculation function 24c may calculate the blood flow rate R1 of finer time phases, and then specify the blood flow rate R1 corresponding to the time phases T1 to T6.

[0042] Next, step S3 will be described. For example, the calculation function 24c acquires an ultrasonic image I2 from the DB as shown in FIG. 4, and calculates the blood flow rate R2 passing through the mitral valve based on the ultrasonic image I2. Here, any known method can be used to calculate the blood flow rate R2. For example, the calculation function 24c can calculate the blood flow rate R2 passing through the mitral valve based on the pulsed Doppler method, the PISA (Proximal Isovelocity Surface Area) method, or the like.

[0043] For example, when imaging by an ultrasound diagnostic device, ultrasound transmitted from an ultrasound probe to a subject is reflected by a discontinuous surface of acoustic impedance, and when reflected by a moving body such as blood, the ultrasound undergoes frequency shift due to the Doppler effect and is received as reflected wave data. Based on such reflected wave data, the ultrasound diagnostic device can generate a Doppler image showing motion information of the moving body as an ultrasound image I2. The Doppler image is, for example, data showing the moving direction and moving speed of blood at each position. That is, the Doppler image is data showing the velocity vector at each position. The calculation function 24c can calculate the blood flow rate R2 passing through the mitral valve based on the Doppler image captured of the mitral valve. Specifically, the calculation function 24c can calculate the blood flow rate R2 by identifying the region of the mitral valve and integrating the velocity vector at each position in the region with respect to time and position.

[0044] Here, the ultrasound image I2 may be collected for each of the time phases T1 to T6, may be collected for a part of the time phases T1 to T6, or may be collected for a time phase other than the time phases T1 to T6. The calculation function 24c may calculate the blood flow rate R2 for all the time phases in which the ultrasound image I2 is collected, or may calculate the blood flow rate R2 only for the time phases T1 to T6.

[0045] Next, the evaluation function 24d evaluates the phase (step S4). Step S4 will be described below with reference to Fig. 5. Fig. 5 is a flow chart showing the details of step S4 in Fig. 2.

[0046] First, the evaluation function 24d performs time normalization for the blood flow rate R1 based on the X-ray CT image I1 calculated in step S2 and the blood flow rate R2 based on the ultrasound image I2 calculated in step S3 (step S401). For example, the evaluation function 24d associates the blood flow rate R1 based on the X-ray CT image I1 with the blood flow rate R2 based on the ultrasound image I2 based on the movement of the heart. Specifically, the evaluation function 24d associates the blood flow rate R1 with the blood flow rate R2 based on an electrocardiogram (characteristic electrocardiogram information such as an R wave). Here, the blood flow rate R1 and the blood flow rate R2 may have different time resolutions. For example, when the blood flow rate R1 based on the X-ray CT image I1 is calculated for the time phases T1 to T6, the blood flow rate R2 based on the ultrasound image I2 may be calculated at intervals smaller than the time phases T1 to T6. Here, by assuming that the subject's RR interval is constant, the evaluation function 24d can normalize and associate the time intervals of each blood flow volume based on the ratio of the elapsed time from a characteristic signal such as an R wave to the RR interval.

[0047] Next, the evaluation function 24d normalizes the blood flow information (step S402). That is, the evaluation function 24d normalizes the magnitude of the blood flow so that the blood flow R1 based on the X-ray CT image I1 and the blood flow R2 based on the ultrasound image I2 can be compared.

[0048] For example, the evaluation function 24d converts the blood flow rate R1 and the blood flow rate R2 so that the maximum value is 1 and the minimum value is 0. Specifically, the blood flow rate R1 or the blood flow rate R2 in the time phase X is set as the blood flow rate Qx, and the blood flow rate Qx is converted to the blood flow rate Yx based on the following formula (1). Q max is the maximum blood flow rate, and Q min is the minimum value of blood flow.

[0049]

number

[0050] The method based on the above formula (1) is just one example, and any method may be used for the normalization in step S402 as long as it enables the information obtained in step S2 and the information obtained in step S3 to be compared on the same basis.

[0051] In step S402, the information obtained in step S2 and the information obtained in step S3 may be standardized to be comparable. More specifically, the blood flow rate at each time point is converted so that the average value is 0 and the variance is 1. That is, the blood flow rate R1 or R2 in time phase X is taken as the blood flow rate Qx, and the blood flow rate Qx is converted to the blood flow rate Zx based on the following formula (2). Q ave is the average value of blood flow in each time phase, and QSD is the standard deviation of blood flow in each time phase.

[0052]

number

[0053] Next, the evaluation function 24d calculates the degree of agreement between the blood flow based on the X-ray CT image I1 and the blood flow based on the ultrasound image I2 (step S403). That is, the evaluation function 24d compares the blood flow R1 of each time phase normalized or standardized in steps S401 and S402 with the blood flow R2 of each time phase normalized or standardized in steps S401 and S402, and calculates the degree of agreement at each time phase.

[0054] FIG. 6 shows an example of the blood flow rate R1 based on the X-ray CT image I1 and the blood flow rate R2 based on the ultrasound image I2. In FIG. 6, the normalized or standardized blood flow rate R1 is shown by a solid line, and the normalized or standardized blood flow rate R2 is shown by a dashed line. For example, the evaluation function 24d can calculate the difference value between the blood flow rate R1 and the blood flow rate R2 for each of the time phases T1 to T6, and use the difference value as the degree of agreement. Also, for example, the evaluation function 24d may normalize the difference value with a maximum value of 1 and a minimum value of 0 to make it easier to compare. For example, as shown in FIG. 6, the evaluation function 24d may normalize the difference value so that the degree of agreement is "1" when the difference value between the blood flow rate R1 and the blood flow rate R2 is "0", and the degree of agreement approaches "0" as the difference value between the blood flow rate R1 and the blood flow rate R2 increases.

[0055] Also, for example, the evaluation function 24d may determine a range of difference values ​​in advance and assign values ​​as the degree of agreement discretely to difference values ​​in a certain range. For example, the evaluation function 24d may assign a degree of agreement of "5" when the difference value between the blood flow rate R1 and the blood flow rate R2 is "0 to 1 [ml / s]", a degree of agreement of "4" when the difference value is "1 to 2 [ml / s]", a degree of agreement of "3" when the difference value is "2 to 3 [ml / s]", a degree of agreement of "2" when the difference value is "3 to 4 [ml / s]", a degree of agreement of "1" when the difference value is "4 to 5 [ml / s]", and a degree of agreement of "0" when the difference value is "5 [ml / s] or more".

[0056] Although the case where the degree of agreement is evaluated based on the difference between the blood flow rate R1 and the blood flow rate R2 has been described, any method may be used as long as it is possible to evaluate the similarity between the blood flow rate R1 and the blood flow rate R2. For example, the degree of agreement may be evaluated based on the ratio of the blood flow rate R2 to the blood flow rate R1.

[0057] Here, the output function 24e may output the various blood flow rates and the degree of agreement described above. For example, the output function 24e displays the blood flow rates and the degree of agreement on the display 22 in response to an instruction from the user. For example, the output function 24e may display the normalized or standardized blood flow rates R1 and R2, and the change in the degree of agreement in the time direction as a graph, as shown in FIG. 6. The calculation function 24c may calculate the values ​​of the blood flow rates R1 and R2 only for the time phases T1 to T6, and the output function 24e may display the blood flow rates R1 and R2, and the change in the degree of agreement in the time direction as a line graph, as shown in FIG. 7. In addition, although an example in which the blood flow rates R1 and R2 are normalized or standardized and displayed is shown in FIG. 6 and FIG. 7, the output function 24e may display the blood flow rates R1 and R2 in a state before normalization or standardization, as shown in FIG. 8. 7 and 8 are diagrams showing display examples according to the first embodiment.

[0058] Next, the evaluation function 24d specifies a phase of interest based on the calculated degree of agreement (step S404). For example, in the case shown in Fig. 6, the evaluation function 24d specifies the phase T2 having the highest degree of agreement as the phase of interest.

[0059] A time phase with a high degree of agreement indicates that the blood flow rates calculated by a plurality of methods are relatively similar, and therefore indicates a more reliable blood flow rate. For example, the blood flow rate R1 based on the X-ray CT image I1 is calculated using an electric circuit model. Also, the blood flow rate R2 based on the ultrasound image I2 is calculated based on the Doppler method. In this way, the blood flow rates R1 and R2 are calculated under different calculation algorithms, and each blood flow rate contains an error specific to the calculation algorithm. Also, the X-ray CT image I1 and the ultrasound image I2 may contain image artifacts or noise, and the blood flow rate in the time phase where the image artifact or noise occurs may deviate from the correct value. Here, when the blood flow rates R1 and R2 match, the influence of errors specific to the calculation algorithm and image artifacts is small, and both the blood flow rates R1 and R2 often have correct values. Therefore, a time phase with a high degree of agreement is a time phase where a reliable result is obtained, and the evaluation function 24d can identify such a time phase as a time phase that requires attention.

[0060] Of course, any method may be used to identify the phase of interest based on the calculated degree of agreement. For example, the evaluation function 24d may identify a period during which the degree of agreement is high as the phase of interest if the period is longer than a threshold value. Also, for example, the evaluation function 24d may identify a phase during the diastole of the heart during which the degree of agreement is high as the phase of interest.

[0061] Continuing the explanation by returning to Fig. 2, the calculation function 24c calculates a measurement value based on the medical image data of the phase of interest specified by the evaluation function 24d, and the output function 24e causes the display 22 to display the measurement value.

[0062] That is, the calculation function 24c calculates the measurement value based on at least one of the X-ray CT image I1 and the ultrasound image I2 in the time phase of interest. For example, the calculation function 24c calculates the area of ​​the mitral valve leaflets based on the area of ​​the mitral valve identified in the X-ray CT image I1 in the time phase of interest in step S2. As other examples of the measurement value related to the mitral valve, the calculation function 24c may calculate the valve annulus circumference, the anterior leaflet length, the posterior leaflet length, etc., or the valve orifice area.

[0063] The calculation function 24c may also calculate morphological characteristics related to the relationship with the organs around the mitral valve. For example, the calculation function 24c may calculate the angle with the aortic valve, the shortest distance from the annulus to the coronary artery, etc. For example, the calculation function 24c may also calculate the distance from the foramen ovale to the center of gravity of the mitral valve.

[0064] Although an example in which the measurement value is calculated and displayed based on the time phase of interest has been described, the output function 24e can also display morphological information other than the measurement value. For example, the output function 24e may select at least one of the X-ray CT image I1 and the ultrasound image I2 in the time phase of interest and display it on the display 22. For example, when a 4D-CT image is collected, the X-ray CT image I1 in each time phase is displayed in order on the display 22. Here, the output function 24e may control so that the X-ray CT image I1 in the time phase of interest is displayed first.

[0065] As described above, according to the first embodiment, the acquisition function 24b acquires the first medical image data acquired based on the first imaging technique and the second medical image data acquired based on the second imaging technique different from the first imaging technique at a predetermined time phase. The calculation function 24c calculates the first fluid information based on the first medical image data and the second fluid information based on the second medical image data. The evaluation function 24d evaluates the predetermined time phase based on the first fluid information and the second fluid information at the predetermined time phase. This allows the medical image processing device 20 to provide more useful information regarding the fluid information calculated based on the medical image data.

[0066] For example, the medical image processing device 20 can identify a time phase of interest based on the degree of agreement between fluid information based on different imaging techniques, and notify the user that the reliability of the fluid information at the time phase of interest is high. Furthermore, the medical image processing device 20 can provide the user with morphological information such as measurement values ​​and medical image data at the time phase of interest. In this way, the medical image processing device 20 can provide more useful information, such as highly reliable fluid information and morphological information, regarding the fluid information calculated based on medical image data.

[0067] In addition, it has been difficult to select an appropriate cardiac phase for evaluating the morphology and fluid state of a cardiac valve such as the mitral valve. In response to this, the medical image processing device 20 can identify a phase in which highly reliable fluid information is obtained as a phase of interest and present it to a user. Based on the phase of interest, the user can more appropriately perform diagnosis, treatment planning, treatment effect evaluation, and the like.

[0068] Second embodiment In the first embodiment, a case where a predetermined time phase is evaluated based on the first fluid information and the second fluid information at the predetermined time phase is described. In contrast, a case where a region based on at least one of the first medical image data and the second medical image data is evaluated is described. The medical image processing system 1 according to the second embodiment has a configuration similar to that of the medical image processing system 1 shown in FIG. 1, and some of the processes by the acquisition function 24b, the calculation function 24c, the evaluation function 24d, and the output function 24e are different. Hereinafter, the points described in the above embodiment are assigned the same reference numerals as those in FIG. 1, and the description will be omitted.

[0069] In the second embodiment, similarly to step S1 in FIG. 2, the acquisition function 24b acquires medical image data. However, the acquisition function 24b does not have to acquire medical image data of multiple time phases. For example, the acquisition function 24b may acquire a CT image of one time phase as the X-ray CT image I1, instead of a 4D-CT image. That is, if the time phases correspond to each other between the first medical image data and the second medical image data, the acquisition function 24b may acquire only one time phase of the first medical image data and the second medical image data.

[0070] Also, similar to step S2 in FIG. 2, the calculation function 24c calculates fluid information based on the X-ray CT image I1. For example, the calculation function 24c calculates the blood flow rate R1 passing through the mitral valve. Although the calculation method of the blood flow rate R1 is arbitrary as described above, the calculation function 24c calculates the blood flow rate R1 at each position of the mitral valve orifice. That is, the calculation function 24c obtains the distribution of the blood flow rate at the mitral valve orifice, not the total flow rate at the mitral valve, as the blood flow rate R1. For example, the calculation function 24c calculates the blood flow rate R1 for each pixel corresponding to the area indicated by the valve orifice. Also, for example, the calculation function 24c divides the area indicated by the valve orifice based on a certain criterion, and calculates the blood flow rate R1 for each divided area. That is, the calculation function 24c does not calculate the blood flow rate of one area for one medical image data, but divides a target area such as the area indicated by the valve orifice into multiple areas, and calculates the blood flow rate R1 for each divided area.

[0071] 2, the calculation function 24c calculates fluid information based on the ultrasonic image I2. For example, the calculation function 24c calculates the blood flow rate R2 passing through the mitral valve. As described above, the calculation function 24c may calculate the blood flow rate R2 at each position of the mitral valve orifice, similar to the blood flow rate R1, although the calculation function 24c may calculate the blood flow rate R2 at each position of the mitral valve orifice. For example, the calculation function 24c calculates the blood flow rate R2 for each pixel corresponding to the region indicated by the valve orifice, based on the pixel value of the Doppler image.

[0072] Also, instead of step S4 in FIG. 2, the evaluation function 24d evaluates a region based on at least one of the X-ray CT image I1 and the ultrasound image I2. For example, the evaluation function 24d evaluates a region in the X-ray CT image I1. Also, for example, the evaluation function 24d evaluates a region in the ultrasound image I2. Here, when aligning the X-ray CT image I1 and the ultrasound image I2, the region in the X-ray CT image I1 and the region in the ultrasound image I2 become equivalent, and there is no need to distinguish them. Hereinafter, a case where the X-ray CT image I1 and the ultrasound image I2 are aligned will be described. A known deformation alignment method can be used for aligning the X-ray CT image I1 and the ultrasound image I2. Examples of such deformation alignment methods include the FFD (Free-Form Deformation) method and the LDDMM (Large Deformation Diffeomorphic Metric Mapping) method.

[0073] Moreover, the evaluation function 24d normalizes the blood flow information, similarly to step S402 in Fig. 5. For example, the evaluation function 24d normalizes or standardizes the magnitude of the blood flow to make it possible to compare the blood flow R1 and the blood flow R2 in each time phase.

[0074] Next, the evaluation function 24d compares the normalized or standardized blood flow rate R1 with the normalized or standardized blood flow rate R2 to calculate the degree of match. This process can be performed for each region obtained by dividing the target region. In other words, this process can be performed for each position in the target region. For example, this process can be performed with the center position of each region obtained by dividing the target region as the calculation point.

[0075] For example, the evaluation function 24d can calculate the difference between the blood flow rate R1 and the blood flow rate R2 for each region obtained by dividing the target region, and use the difference as the degree of agreement for each region. For example, the evaluation function 24d can normalize the difference value by setting the maximum value to 1 and the minimum value to 0 to make it easier to compare. For example, the evaluation function 24d can determine a range of difference values ​​in advance and assign values ​​as the degree of agreement discretely to difference values ​​within a certain range. Note that, although the case where the degree of agreement is evaluated by the difference between the flow rate R1 and the blood flow rate R2 has been described, any method can be used as long as it can evaluate the similarity between the blood flow rate R1 and the blood flow rate R2.

[0076] Here, the output function 24e may output the various blood flow amounts and the degree of agreement described above. For example, the output function 24e causes the display 22 to display the blood flow amounts and the degree of agreement in response to an instruction from the user.

[0077] For example, the output function 24e can display an image showing the distribution of the degrees of coincidence as shown in Fig. 9. Specifically, Fig. 9 illustrates the area of ​​the mitral valve. For example, the output function 24e generates a mesh showing the shape of the area of ​​the mitral valve identified based on the X-ray CT image I1, and displays it on the display 22. Furthermore, the output function 24e superimposes the mesh and displays the distribution of the degrees of coincidence at each position of the mitral valve orifice in different colors.

[0078] In FIG. 9, the degree of agreement is shown in two colors. Specifically, in FIG. 9, the degree of agreement is calculated in a numerical range of "0 to 1", and an area in the mitral valve orifice where the degree of agreement is "0.5 to 1" and an area where the degree of agreement is "0 to 0.5" are illustrated by different colors. Here, the output function 24e may highlight positions with high degrees of agreement. For example, in FIG. 9, the output function 24e may display areas where the degree of agreement is "0.5 to 1" in a color that stands out more than areas where the degree of agreement is "0 to 0.5". Also, for example, in FIG. 9, the output function 24e may color areas where the degree of agreement is "0.5 to 1" and not color areas where the degree of agreement is "0 to 0.5".

[0079] Of course, various modifications are possible regarding the display mode. For example, the output function 24e may divide the numerical range of "0 to 1" into three or more stages and illustrate the distribution of the degree of agreement in three or more colors. Also, for example, the output function 24e may illustrate the degree of agreement calculated with a continuous value in a color that changes continuously. That is, the output function 24e may show the degree of agreement in a gradation. Also, in FIG. 9, a case where the degree of agreement is illustrated by superimposing it on a mesh showing the shape of the mitral valve region is shown, but the output function 24e may illustrate the degree of agreement by superimposing it on the X-ray CT image I1 or the ultrasound image I2. For example, as shown in FIG. 10, the output function 24e may display a cross section in which the mitral valve appears in the X-ray CT image I1 and superimpose the degree of agreement on the cross section. Also, the output function 24e may display a cross section in which the mitral valve appears in the X-ray CT image I1 and superimpose the mesh showing the shape of the mitral valve region and the degree of agreement on the cross section.

[0080] The output function 24e may also illustrate the blood flow rate instead of or in addition to illustrating the degree of coincidence. For example, the output function 24e can illustrate the distribution of the blood flow rate R1 by superimposing it on the mesh or X-ray CT image I1 shown in FIG. 9. For example, the output function 24e can illustrate the distribution of the blood flow rate R2 by superimposing it on the mesh or ultrasound image I2 shown in FIG. 9.

[0081] Next, the evaluation function 24d specifies the region of interest based on the calculated degree of agreement. For example, the evaluation function 24d specifies the region with the highest degree of agreement as the region of interest. A region with a high degree of agreement indicates that the blood flow rates calculated by a plurality of methods are relatively similar, and therefore indicates a more reliable blood flow rate. Of course, any method may be used as long as the region of interest is specified based on the calculated degree of agreement. For example, the evaluation function 24d may specify a continuous region with a degree of agreement exceeding a threshold and a predetermined area as the region of interest.

[0082] Next, the output function 24e highlights the specified region of interest. For example, the output function 24e may reconstruct and display a cross section in which the specified region of interest is displayed most large. Specifically, when the region of interest is specified as a plane, the output function 24e performs MPR (Multi-Planar Reconstruction) processing on a cross section that is parallel to the region of interest and passes through the region of interest, and displays the MPR image on the display 22. That is, the output function 24e displays a cross section including the region of interest. The output function 24e may also change the magnification ratio and enlarge and display the region of interest.

[0083] The identified region of interest can also be used when performing various post-analyses. For example, only the region of interest may be the subject of analysis. For example, the blood flow rate in the region of interest may be summed up, and then multiplied by a factor corresponding to the ratio of the valve orifice area to the area of ​​the region of interest to calculate the blood flow rate in the subject.

[0084] As described above, according to the second embodiment, the calculation function 24c calculates the first fluid information based on the first medical image data and the second fluid information based on the second medical image data. The evaluation function 24d calculates the degree of agreement between the first fluid information and the second fluid information, and identifies the region of interest based on the degree of agreement. This allows the medical image processing device 20 to provide more useful information regarding the fluid information calculated based on the medical image data.

[0085] For example, the medical image processing device 20 can specify a region of interest based on the degree of agreement between fluid information based on different imaging techniques, and notify the user that the reliability of the fluid information in the region of interest is high. Furthermore, the medical image processing device 20 can highlight the region of interest and provide the user with its morphological information. This allows the user to easily confirm the location of more reliable fluid information. The medical image processing device 20 can also provide the user with the results of post-analysis using the region of interest. In this way, the medical image processing device 20 can provide more useful information regarding the fluid information calculated based on medical image data.

[0086] In the above-described embodiment, the blood flow rate has been described as an example of fluid information, but the embodiment is not limited thereto. For example, the calculation function 24c may calculate the blood flow rate in a range of a predetermined direction as the blood flow information. More specifically, the calculation function 24c may calculate the regurgitant flow rate or the forward flow rate in the mitral valve as the blood flow information. Also, for example, the calculation function 24c may calculate the blood flow rate passing through a part of an area or position of the mitral valve, instead of the total amount of blood flow passing through the mitral valve, as the blood flow information. Also, for example, the calculation function 24c may calculate the flow velocity as the blood flow information. As an example, the calculation function 24c may calculate the average or median value of the flow velocity of the blood flow passing through the mitral valve.

[0087] In addition, although the embodiment has been described as being implemented on the mitral valve, the embodiment is not limited thereto. For example, the embodiment can be similarly applied to blood flow in various blood vessels, food flow in the esophagus, urine flow in the ureter, cerebrospinal fluid flow in the head, etc. In other words, the embodiment described above can be applied to any biological organ in which the first and second fluid information can be calculated based on medical image data obtained by different imaging techniques.

[0088] In addition, although the case where the calculation function 24c calculates the fluid information based on the X-ray CT image I1 and the fluid information based on the ultrasound image I2 has been described, the embodiment is not limited to this. For example, when the fluid information is calculated at the time of capturing each medical image data and stored in a DB of an electronic medical record system, the calculation function 24c may acquire the fluid information from the DB instead of calculating the fluid information.

[0089] In addition, as examples of the first fluid information and the second fluid information, the fluid information based on the X-ray CT image I1 and the fluid information based on the ultrasound image I2 have been described, but the embodiment is not limited thereto. For example, instead of the fluid information based on the X-ray CT image I1, the fluid information based on the MRI image may be calculated. For example, the fluid information based on the MRI image may be calculated in step S2 of FIG. 2, and the evaluation of the time phase in step S4 may be performed based on the fluid information and the fluid information based on the ultrasound image I2.

[0090] In addition, in the first embodiment, a case where a phase of interest is specified is described, and in the second embodiment, a case where a region of interest is specified is described, but both a phase of interest and a region of interest may be specified. That is, the evaluation function 24d may specify both a predetermined phase and a region based on at least one of the first medical image data and the second medical image data based on the first fluid information and the second fluid information. For example, the evaluation function 24d may calculate a degree of agreement between the first fluid information and the second fluid information, and specify a specific region in a specific phase as a phase of interest and a region of interest based on the degree of agreement.

[0091] In addition, although the above description has been given of the case where the first medical image data and the second medical image data are collected by different medical modalities, the first medical image data and the second medical image data may be collected by the same type of medical modality as long as they are collected by different imaging techniques. For example, the first medical image data and the second medical image data may be X-ray CT images collected under different contrast conditions. For example, the first medical image data and the second medical image data may be X-ray CT images reconstructed by different reconstruction methods. For example, the first medical image data and the second medical image data may be MRI images collected by different pulse sequences.

[0092] The term "processor" used in the above description means a circuit such as a CPU, a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). When the processor is, for example, a CPU, the processor realizes a function by reading and executing a program stored in a memory circuit. On the other hand, when the processor is, for example, an ASIC, instead of storing a program in a memory circuit, the function is directly incorporated as a logic circuit in the circuit of the processor. Note that each processor in the embodiment is not limited to being configured as a single circuit for each processor, but may be configured as one processor by combining multiple independent circuits to realize the function. Furthermore, multiple components in each figure may be integrated into one processor to realize the function.

[0093] 1, a single memory 21 is described as storing a program corresponding to each processing function of the processing circuit 24. However, the embodiment is not limited to this. For example, a configuration may be adopted in which a plurality of memories 21 are distributed and the processing circuit 24 reads out the corresponding program from each memory 21. Also, instead of storing the program in the memory 21, the program may be directly built into the circuit of the processor. In this case, the processor realizes the function by reading and executing the program built into the circuit.

[0094] Each component of each device according to the above-mentioned embodiment is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or a part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc. Furthermore, each processing function performed by each device can be realized in whole or in any part by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0095] The medical image processing method described in the above-mentioned embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. This program can be distributed via a network such as the Internet. This program can also be recorded on a non-transitory recording medium readable by a computer such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, or a DVD, and executed by being read from the recording medium by a computer.

[0096] According to at least one of the embodiments described above, it is possible to provide more useful information regarding fluid information calculated based on medical image data.

[0097] Although some 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 in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0098] 1 Medical image processing system 10 Medical imaging diagnostic equipment 20 Medical image processing device 21 Memory 22 Display 23 Input Interface 24 Processing circuit 24a Control Functions 24b Acquisition function 24c calculation function 24d rating function 24e Output Function 30 Image Archive Device

Claims

1. an acquisition unit that acquires, at a predetermined time phase, first medical image data acquired based on a first imaging technique and second medical image data acquired based on a second imaging technique different from the first imaging technique; A calculation unit that calculates first fluid information, which is a flow rate or flow velocity based on the first medical image data, and second fluid information, which is a flow rate or flow velocity based on the second medical image data; an evaluation unit that performs time normalization and normalization or standardization of blood flow information between the first fluid information and the second fluid information at the predetermined time phase, calculates a degree of agreement between the first fluid information and the second fluid information, and identifies a time phase of interest from the predetermined time phase based on the degree of agreement; A medical image processing device comprising:

2. The calculation unit calculates the first fluid information based on the first medical image data of a plurality of time phases, and calculates the second fluid information based on the second medical image data of a plurality of time phases; The medical image processing apparatus according to claim 1 , wherein the evaluation unit identifies a time phase of interest from at least the predetermined time phases based on the first fluid information and the second fluid information.

3. further comprising an output unit that performs output according to the evaluation by the evaluation unit; The calculation unit calculates a measurement value based on at least one of the first medical image data and the second medical image data in the time phase of interest, The medical image processing apparatus according to claim 1 , wherein the output unit displays the measurement value.

4. The medical image processing apparatus according to claim 1 , further comprising an output unit that displays at least one of the first medical image data and the second medical image data in the time phase of interest.

5. An acquisition unit that acquires first medical image data acquired based on a first imaging technique and second medical image data acquired based on a second imaging technique different from the first imaging technique at a predetermined time phase; A calculation unit that calculates first fluid information, which is a flow rate or flow velocity based on the first medical image data, and second fluid information, which is a flow rate or flow velocity based on the second medical image data; an evaluation unit that normalizes or standardizes blood flow information between the first fluid information and the second fluid information at the predetermined time phase, aligns the first medical image data and the second medical image data, calculates a difference value between the first fluid information and the second fluid information for each divided region obtained by dividing a target region as a degree of agreement between the first fluid information and the second fluid information at the predetermined time phase, and identifies a region of interest from a region based on the first medical image data and the second medical image data based on the degree of agreement; A medical image processing device comprising:

6. The medical image processing apparatus according to claim 5 , further comprising an output unit that highlights the region of interest in the first medical image data and the second medical image data.

7. the first medical image data and the second medical image data are three-dimensional data; The medical image processing apparatus according to claim 6 , wherein the output unit displays a cross section including the region of interest in the three-dimensional data.

8. Acquiring first medical image data based on a first imaging technique and second medical image data based on a second imaging technique different from the first imaging technique at a predetermined time phase; Calculating first fluid information, which is a flow rate or flow velocity based on the first medical image data, and second fluid information, which is a flow rate or flow velocity based on the second medical image data; Normalizing time and normalizing or standardizing blood flow information between the first fluid information and the second fluid information in the predetermined time phase, calculating a degree of agreement between the first fluid information and the second fluid information, and identifying a time phase of interest from the predetermined time phase based on the degree of agreement. A medical image processing method comprising:

9. A method for obtaining first medical image data based on a first imaging technique and second medical image data based on a second imaging technique different from the first imaging technique, at a predetermined time phase; Calculating first fluid information, which is a flow rate or flow velocity based on the first medical image data, and second fluid information, which is a flow rate or flow velocity based on the second medical image data; Normalizing or standardizing blood flow information between the first fluid information and the second fluid information at the specified time phase, aligning the first medical image data and the second medical image data, calculating a difference value between the first fluid information and the second fluid information for each divided region obtained by dividing a target region as a degree of agreement between the first fluid information and the second fluid information at the specified time phase, and identifying a region of interest from a region based on the first medical image data and the second medical image data based on the degree of agreement. A medical image processing method comprising:

10. Acquiring first medical image data based on a first imaging technique and second medical image data based on a second imaging technique different from the first imaging technique at a predetermined time phase; Calculating first fluid information, which is a flow rate or flow velocity based on the first medical image data, and second fluid information, which is a flow rate or flow velocity based on the second medical image data; Normalizing time and normalizing or standardizing blood flow information between the first fluid information and the second fluid information in the predetermined time phase, calculating a degree of agreement between the first fluid information and the second fluid information, and identifying a time phase of interest from the predetermined time phase based on the degree of agreement. A program that causes a computer to execute each process.

11. A method for obtaining first medical image data based on a first imaging technique and second medical image data based on a second imaging technique different from the first imaging technique, at a predetermined time phase; Calculating first fluid information, which is a flow rate or flow velocity based on the first medical image data, and second fluid information, which is a flow rate or flow velocity based on the second medical image data; Normalizing or standardizing blood flow information between the first fluid information and the second fluid information at the specified time phase, aligning the first medical image data and the second medical image data, calculating a difference value between the first fluid information and the second fluid information for each divided region obtained by dividing a target region as a degree of agreement between the first fluid information and the second fluid information at the specified time phase, and identifying a region of interest from a region based on the first medical image data and the second medical image data based on the degree of agreement. A program that causes a computer to execute each process.

Images