Medical image processing apparatus, medical image processing method, and medical image processing program

Abstract

To provide a medical image processing apparatus which can perform a fluid analysis of a blood flow with higher accuracy, a medical image processing method, and medical image processing program.SOLUTION: A medical image processing apparatus according to the embodiment includes an acquisition part, an analysis part, and a specifying part. The acquisition part acquires image data including a blood vessel of a subject. The analysis part obtains a spatial distribution of blood flow parameters of the blood vessel by performing a fluid analysis using the image data. The specifying part calculates a degree of reliability related to the fluid analysis by performing an image analysis related to the blood vessel by using the image data, detects a branch which is the blood vessel branching from the blood vessel based on the image data, and calculates a blood vessel diameter of the branch in a connecting part of the branch and the blood vessel. When the blood vessel diameter is equal to or more than a prescribed threshold value, the specifying part displays information indicating a region including the connecting part with a lower degree of reliability in the fluid analysis and a warning message.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to a medical image processing apparatus, a medical image processing method, and a medical image processing program.

Background Art

[0002] Conventionally, a technique for performing fluid analysis of blood flow by simulation using image data collected by a medical image diagnostic apparatus such as an X-ray CT (Computed Tomography) apparatus or an MRI (Magnetic Resonance Imaging) apparatus is known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem to be solved by the present invention is to provide a medical image processing apparatus, a medical image processing method, and a medical image processing program capable of performing fluid analysis of blood flow with higher accuracy.

Means for Solving the Problems

[0005] The medical image processing apparatus according to the embodiment includes an acquisition unit, an analysis unit, and a specification unit. The acquisition unit acquires image data including blood vessels of a subject. The analysis unit obtains the spatial distribution of blood flow parameters in the blood vessels by performing fluid analysis using the image data. The specification unit calculates the reliability regarding the fluid analysis by performing image analysis regarding the blood vessels using the image data, detects a branch that is a blood vessel branching from the blood vessel based on the image data, calculates the blood vessel diameter of the branch at the connection location between the branch and the blood vessel, and when the blood vessel diameter is equal to or greater than a predetermined threshold value, displays information indicating a region with low reliability of the fluid analysis including the connection location and a warning message.

Brief Description of Drawings

[0006]

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DETAILED DESCRIPTION OF THE INVENTION

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

[0008] (First Embodiment) FIG. 1 is a diagram showing a configuration example of a medical image processing apparatus according to the first embodiment. For example, as shown in FIG. 1, a medical image processing apparatus 100 according to the present embodiment is communicably connected to an X-ray CT (Computed Tomography) apparatus 300 and a medical image storage apparatus 400 via a network 200. Note that the medical image processing apparatus 100 may be further connected to other medical image diagnostic apparatuses such as an MRI (Magnetic Resonance Imaging) apparatus, an X-ray diagnostic apparatus, an ultrasonic diagnostic apparatus, and a PET (Positron Emission Tomography) apparatus via the network 200.

[0009] The X-ray CT apparatus 300 collects CT image data regarding a subject. Specifically, the X-ray CT apparatus 300 rotates an X-ray tube and an X-ray detector around the subject substantially at the center, detects the X-rays transmitted through the subject, and collects projection data. Then, the X-ray CT apparatus 300 generates two-dimensional or three-dimensional CT image data based on the collected projection data.

[0010] The medical image storage device 400 acquires CT image data and projection data from the X-ray CT device 300 via the network 200, and stores the acquired CT image data and projection data in a storage circuit provided inside or outside the device. For example, the medical image storage device 400 is realized by a computer device such as a server device.

[0011] The medical image processing device 100 acquires CT image data from the X-ray CT device 300 or the medical image storage device 400 via the network 200, and performs various image processes on the acquired CT image data. The medical image processing device 100 is realized by a computer device such as a workstation.

[0012] For example, the medical image processing device 100 includes an I / F (interface) circuit 110, a storage circuit 120, an input circuit 130, a display 140, and a processing circuit 150.

[0013] The I / F circuit 110 is connected to the processing circuit 150 and controls the transmission and communication of various data performed between the X-ray CT device 300 and the medical image storage device 400. In this embodiment, the I / F circuit 110 receives CT image data from the X-ray CT device 300 or the medical image storage device 400, and outputs the received CT image data to the processing circuit 150. For example, the I / F circuit 110 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

[0014] The storage circuit 120 is connected to the processing circuit 150 and stores various data. In this embodiment, the storage circuit 120 stores the CT image data received from the X-ray CT device 300 or the medical image storage device 400. For example, the storage circuit 120 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.

[0015] The input circuit 130 is connected to the processing circuit 150, converts the input operation received from the operator into an electrical signal, and outputs it to the processing circuit 150. For example, the input circuit 130 is realized by a trackball, a switch button, a mouse, a keyboard, a touch panel, or the like.

[0016] The display 140 is connected to the processing circuit 150 and displays various information and various image data output from the processing circuit 150. For example, the display 140 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like.

[0017] The processing circuit 150 controls each component of the medical image processing apparatus 100 according to the input operation received from the operator via the input circuit 130. Specifically, the processing circuit 150 causes the storage circuit 120 to store the CT image data output from the I / F circuit 110. Further, the processing circuit 150 reads the CT image data from the storage circuit 120 and displays it on the display 140. For example, the processing circuit 150 is realized by a processor.

[0018] The overall configuration of the medical image processing apparatus 100 according to the present embodiment has been described above. Under such a configuration, the medical image processing apparatus 100 according to the present embodiment has a function of performing fluid analysis of blood flow by simulation using three-dimensional CT image data including the blood vessels of a subject. For example, the medical image processing apparatus 100 according to the present embodiment is used when diagnosing or treating coronary artery stenosis.

[0019] Here, generally, since coronary artery stenosis has multifaceted problems, a large amount of information is required when evaluating coronary artery stenosis. On the other hand, by simulating the dynamics of blood flow by fluid analysis, the severity of stenosis can be identified. However, generally, in the simulation performed by fluid analysis, the accuracy of the analysis result may be lowered when the geometric shape of the blood vessel and the pattern of blood flow are complicated.

[0020] For this reason, the medical image processing apparatus 100 according to the present embodiment is configured to be able to perform fluid analysis of blood flow with higher accuracy. Note that the medical image processing apparatus 100 according to the present embodiment can be similarly used not only when the object of diagnosis or treatment is the coronary artery but also when the blood vessels of other organs such as the brain and liver are the objects.

[0021] Specifically, in the medical image processing apparatus 100 according to the present embodiment, the processing circuit 150 has an acquisition function 151, a specification function 152, and an analysis function 153. Note that the acquisition function 151 is an example of the acquisition unit in the claims. Also, the specification function 152 is an example of the specification unit in the claims. Also, the analysis function 153 is an example of the analysis unit in the claims.

[0022] The acquisition function 151 acquires three-dimensional CT image data including the blood vessels of the subject. Specifically, the acquisition function 151 acquires CT image data from the X-ray CT apparatus 300 or the medical image storage apparatus 400 and stores the acquired CT image data in the storage circuit 120.

[0023] The specification function 152 performs analysis related to blood vessels using the CT image data and specifies a region of interest in the blood vessels of the subject based on the spatial distribution of the analysis results related to the blood vessels obtained by the analysis. Specifically, the specification function 152 reads out the CT image data acquired by the acquisition function 151 from the storage circuit 120 and performs analysis using the read CT image data. Then, the specification function 152 specifies a region of interest in the blood vessels included in the CT image based on the analysis results of the analysis.

[0024] The analysis function 153 performs fluid analysis with a first accuracy for regions other than the region of interest and performs fluid analysis with a second accuracy higher than the first accuracy for the region of interest. Specifically, the analysis function 153 uses the CT image data acquired by the acquisition function 151 and performs fluid analysis with a first accuracy for regions other than the region of interest based on the region of interest specified by the specification function 152, and performs fluid analysis with a second accuracy higher than the first accuracy for the region of interest.

[0025] As described above, the medical image processing apparatus 100 according to the present embodiment identifies a region of interest in a blood vessel based on the analysis result of the analysis related to the blood vessel, and performs a fluid analysis with higher accuracy on the identified region of interest compared to the regions other than the region of interest. Thus, according to the present embodiment, the fluid analysis of the blood flow can be performed with higher accuracy.

[0026] Here, each of the above-described processing functions is stored in the storage circuit 120 in the form of a program executable by, for example, a computer. The processing circuit 150 reads out each program from the storage circuit 120 and executes each read program, thereby realizing the processing function corresponding to each program. In other words, the processing circuit 150 in the state where each program is read out has each processing function shown in FIG. 1.

[0027] In FIG. 1, an example in which each of the above-described processing functions is realized by a single processing circuit 150 has been described, but the embodiment is not limited to this. For example, the processing circuit 150 may be configured by combining a plurality of independent processors, and each processor may execute each program to realize each processing function. Further, each processing function of the processing circuit 150 may be appropriately distributed or integrated into a single or a plurality of processing circuits and realized.

[0028] Hereinafter, the processing performed by the medical image processing apparatus 100 according to the present embodiment will be described in more detail. In the present embodiment, an example in which the specific function 152 performs a fluid analysis as an analysis related to a blood vessel will be described.

[0029] FIG. 2 is a flowchart showing a processing procedure of the fluid analysis performed by the medical image processing apparatus 100 according to the first embodiment.

[0030] For example, as shown in FIG. 2, in the present embodiment, first, the acquisition function 151 acquires three-dimensional CT image data including the blood vessels of the subject from the X-ray CT apparatus 300 or the medical image storage apparatus 400 (step S101). Specifically, when the acquisition function 151 receives an operation instructing the start of fluid analysis from the operator via the input circuit 130, the acquisition function 151 acquires CT image data including the blood vessels of the subject designated by the operator for the subject designated by the operator.

[0031] Subsequently, the specification function 152 performs fluid analysis using the CT image data acquired by the acquisition function 151. Specifically, the specification function 152 performs fluid analysis with a predetermined accuracy on the entire blood vessels included in the CT image data. Here, the specification function 152 performs fluid analysis by simulation using the finite element method.

[0032] First, the specification function 152 generates an analysis model of the entire blood vessels included in the CT image data with a predetermined accuracy based on the CT image data (step S102).

[0033] FIG. 3 is a diagram showing an example of an analysis model generated by the specification function 152 according to the first embodiment. For example, as shown in FIG. 3, the specification function 152 generates an analysis model 31 of the blood vessels by dividing the shape of the blood vessels included in the CT image data into a plurality of triangular meshes. Here, the specification function 152 sets the density of the meshes in the analysis model so that fluid analysis is performed with the above-described predetermined accuracy.

[0034] After that, the specific function 152 executes a fluid analysis using the generated analysis model (step S103). Specifically, the specific function 152 calculates the value of the blood flow parameter as the analysis result of the fluid analysis. Here, the blood flow parameter is, for example, FFR (Fractional Flow Reserve). In addition, as the blood flow parameter, various parameters other than FFR can also be used. For example, the blood flow parameter can be blood flow velocity, velocity gradient, pressure, pressure gradient, pressure ratio, vorticity, kinetic energy, turbulence intensity, shear stress, shear stress gradient, etc.

[0035] Subsequently, the specific function 152 identifies the region of interest in the subject's blood vessel based on the analysis result of the fluid analysis (step S104). Specifically, the specific function 152 identifies the region where the value of the blood flow parameter obtained as the analysis result by the fluid analysis is outside the normal range as the region of interest. Here, the normal range mentioned here is determined in advance for each of the various blood flow parameters described above, and the corresponding range is appropriately used according to the type of blood flow parameter used in the analysis.

[0036] After that, the specific function 152 displays the analysis result of the fluid analysis and the region of interest on the display 140 (step S105). Then, the specific function 152 receives from the operator, via the input circuit 130, an operation to change the region of interest and an operation to instruct whether to execute the analysis of the region of interest.

[0037] FIG. 4 is a diagram showing an example of the analysis result and the region of interest displayed by the specific function 152 according to the first embodiment. For example, as shown in FIG. 4, the specific function 152 displays a graphic 41 representing the shape of the analysis model on the display 140. Then, the specific function 152 displays information indicating the distribution of the values of the blood flow parameters obtained by the fluid analysis on the graphic 41 representing the analysis model. For example, the specific function 152 assigns different colors to each value of the blood flow parameter and colors the graphic 41 representing the analysis model according to the spatial distribution of the values of the blood flow parameter within the region of interest. In addition, the specific function 152 displays a graphic 42 indicating the range of the specified region of interest on the graphic 41 representing the analysis model. Here, when the specific function 152 receives an operation to change the region of interest, it changes the range of the region of interest according to the received operation and displays a graphic 42 representing the changed region of interest on the graphic 41 representing the analysis model.

[0038] Then, when an operation instructing the execution of the analysis of the region of interest is received by the specific function 152 (step S106, Yes), the analysis function 153 executes a fluid analysis based on the region of interest set at that time. Here, the analysis function 153 performs a fluid analysis by simulation using the finite element method, similar to the specific function 152.

[0039] Here, in the first fluid analysis, the analysis function 153 performs a fluid analysis with the same predetermined accuracy as the fluid analysis performed by the specific function 152 for the regions other than the region of interest, and performs a fluid analysis with a higher accuracy than the predetermined accuracy for the region of interest.

[0040] At this time, the analysis function 153 performs a fluid analysis with a higher accuracy than the predetermined accuracy by increasing the spatial resolution of the image data on which the analysis model used in the fluid analysis is based. In addition, the analysis function 153 performs a fluid analysis with a higher accuracy than the predetermined accuracy by increasing the calculation cost of the fluid analysis. For example, the analysis function 153 increases the calculation cost of the fluid analysis by increasing the density of the mesh in the analysis model of the finite element method.

[0041] Specifically, the analysis function 153 requests the X-ray CT apparatus 300 to reconstruct three-dimensional CT image data in which the range where the region of interest is set at that time is enlarged, and thereby acquires from the X-ray CT apparatus 300 the CT image data obtained by enlarging and reconstructing the region of interest (step S107). Thereby, CT image data with higher spatial resolution for the region of interest can be obtained.

[0042] Subsequently, the analysis function 153 generates an analysis model of the region of interest with a higher mesh density than the analysis model used in the fluid analysis performed by the specific function 152 based on the CT image data obtained by enlarging and reconstructing the region of interest (step S108).

[0043] Here, instead of using the CT image data obtained by enlarging and reconstructing the region of interest, the analysis function 153 may generate an analysis model of the region of interest with a higher mesh density by setting the mesh of the analysis model used in the fluid analysis performed by the specific function 152 to be finer. In this case, it is not necessary to acquire the CT image data obtained by enlarging and reconstructing the region of interest. Further, in this case, after generating the analysis model with a higher mesh density, the analysis function 153 may correct the mesh by comparing the generated analysis model with the CT image data.

[0044] Thereafter, the analysis function 153 generates a composite model by connecting the portion other than the region of interest in the analysis model of the entire blood vessel generated with the aforementioned predetermined accuracy and the analysis model of the region of interest generated with an accuracy higher than the predetermined accuracy (step S109).

[0045] FIG. 5 is a diagram showing an example of a composite model generated by the analysis function 153 according to the first embodiment. For example, as shown in FIG. 5, the analysis function 153 generates a composite model by connecting portions 31a, 31b, and 31c other than the region of interest in the analysis model 31 of the entire blood vessel and the analysis model 51 of the region of interest. By using such a composite model, for the region of interest, a fluid analysis with higher accuracy is performed compared to the portions other than the region of interest.

[0046] Then, the analysis function 153 executes a fluid analysis of the entire blood vessel using the generated composite model (step S110).

[0047] Thereafter, the specifying function 152 specifies the region of interest again based on the analysis result obtained by the newly performed fluid analysis (step S104), and displays the analysis result of the fluid analysis and the region of interest on the display 140 (step S105).

[0048] FIG. 6 is a diagram showing another example of the analysis result and the region of interest displayed by the specifying function 152 according to the first embodiment. For example, as shown in FIG. 6, the specifying function 152, similar to the example shown in FIG. 4, displays a graphic 41 representing the shape of the analysis model, information indicating the distribution of the values of the blood flow parameters obtained by the fluid analysis, and a graphic 42 representing the region of interest. The graphic 41 representing the shape of the analysis model displayed here is displayed based on the composite model used in the newly performed fluid analysis. Also, the information indicating the distribution of the values of the blood flow parameters is displayed based on the analysis result obtained by the newly performed fluid analysis. Further, the graphic 42 representing the region of interest is displayed based on the region of interest set last.

[0049] By referring to the analysis results of the fluid analysis displayed on the display 140, the operator can determine whether to execute the fluid analysis again. Then, when an operation instructing to execute the analysis of the region of interest again is received by the specific function 152 (step S106, Yes), the analysis function 153 executes the fluid analysis again based on the region of interest set at that time.

[0050] Here, in the fluid analysis after the second time, the analysis function 153 performs a fluid analysis with the same accuracy as the previous fluid analysis for regions other than the region of interest, and performs a fluid analysis with a higher accuracy than the accuracy of the previous fluid analysis for the region of interest.

[0051] At this time, the analysis function 153 performs a fluid analysis with a higher accuracy than the accuracy of the previous fluid analysis by increasing the spatial resolution of the image data that is the basis of the analysis model used in the fluid analysis. Also, the analysis function 153 performs a fluid analysis with a higher accuracy than the accuracy of the previous fluid analysis by increasing the calculation cost of the fluid analysis. For example, the analysis function 153 increases the calculation cost of the fluid analysis by increasing the density of the mesh in the analysis model of the finite element method.

[0052] Specifically, the analysis function 153 acquires the CT image data in which the region of interest is expanded and reconstructed from the X-ray CT apparatus 300 in the same manner as when performing the first fluid analysis (step S107). Then, the analysis function 153 generates an analysis model of the region of interest with a higher mesh density than the analysis model used in the previous fluid analysis using the acquired CT image data (step S108).

[0053] After that, the analysis function 153 generates a composite model by connecting the part other than the region of interest in the analysis model of the entire blood vessel generated for the previous fluid analysis and the analysis model of the region of interest generated for the new fluid analysis (step S109). Then, the analysis function 153 executes a fluid analysis of the entire blood vessel using the generated composite model (step S110).

[0054] In this way, while the analysis function 153 is receiving an operation instruction from the specific function 152 to execute the analysis of the region of interest (step S106, Yes), the fluid analysis is repeated. When an operation instruction is received from the specific function 152 not to execute the analysis of the region of interest (step S106, No), the analysis function 153 displays the result of the last fluid analysis on the display 140 as the final result (step S111).

[0055] FIG. 7 is a diagram showing an example of the final result displayed by the analysis function 153 according to the first embodiment. For example, as shown in FIG. 7, the analysis function 153 displays, as the final result, a graphic 41 representing the shape of the analysis model, information indicating the distribution of the values of the blood flow parameters obtained by the fluid analysis, and a graphic 42 representing the region of interest, similar to the examples shown in FIGS. 4 and 6. Here, the graphic 41 representing the shape of the analysis model displayed is based on the composite model used in the last fluid analysis performed. Also, the information indicating the distribution of the values of the blood flow parameters is displayed based on the result of the last fluid analysis performed. Further, the graphic 42 representing the region of interest is displayed based on the region of interest set last.

[0056] Furthermore, for example, the analysis function 153 displays, as a final result, information indicating the values of the geometric shape parameters related to the blood vessel analysis model. Here, the vascular geometric parameters include, for example, the blood vessel centerline, the coronary vessel lumen area, the coronary vessel diameter (size), the minimal luminal area, the minimal luminal diameter (size), the ratio of the minimal luminal area to the coronary vessel area, the ratio of the minimal luminal diameter to the coronary vessel size, the change of the coronary vessel area along the vessel length, the percentage of the coronary vessel area to a pre-defined referenced area, and the like.

[0057] For example, similar to the blood flow parameters, the specific function 152 assigns different colors to each value of the geometric shape parameters and colors the graphic 41 representing the analysis model according to the spatial distribution of the values of the geometric shape parameters within the region of interest.

[0058] Here, for example, the specific function 152 receives from the operator an operation of selecting to display or not display information regarding the blood flow parameter and information regarding the geometric shape parameter respectively via the input circuit 130. Then, according to the received operation, the specific function 152 displays the information selected by the operator on the graphic 41 representing the analysis model. For example, as shown in FIG. 7, the specific function 152 receives from the operator an operation of selecting to display or not display each piece of information by displaying check boxes for selecting to display or not display the blood flow parameter and the geometric shape parameter respectively on the display 140.

[0059] Note that each of the above steps is realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to each function from the storage circuit 120. Specifically, step S101 is realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to the acquisition function 151 from the storage circuit 120. Also, steps S102 to S106 are realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to the specific function 152 from the storage circuit 120. Also, steps S107 to S111 are realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to the analysis function 153 from the storage circuit 120.

[0060] As described above, in the first embodiment, the medical image processing apparatus 100 specifies a region of interest in a blood vessel based on the analysis result of fluid analysis, and performs fluid analysis with higher accuracy on the specified region of interest compared to the region other than the region of interest. Therefore, according to this embodiment, fluid analysis of blood flow can be performed with higher accuracy.

[0061] Note that the above-described first embodiment can also be implemented with appropriate modifications. Hereinafter, a plurality of modification examples according to the first embodiment will be described. Note that the configuration of the medical image processing apparatus according to each modification example is basically the same as the configuration shown in FIG. 1. Therefore, hereinafter, the description will focus on the points different from the content described in the first embodiment.

[0062] (First Modification Example of the First Embodiment) For example, in the above-described first embodiment, an example was described in which the specific function 152 performs fluid analysis as an analysis related to blood vessels and specifies a region of interest in the subject's blood vessels based on the analysis result of the fluid analysis. However, the embodiment is not limited to this. For example, the specific function 152 may perform image analysis as an analysis related to blood vessels and specify a region of interest based on the analysis result of the image analysis.

[0063] FIG. 8 is a flowchart showing a processing procedure of fluid analysis performed by the medical image processing apparatus 100 according to the first modification example of the first embodiment.

[0064] For example, as shown in FIG. 8, in this modification example, first, the acquisition function 151 acquires three-dimensional CT image data including the subject's blood vessels from the X-ray CT apparatus 300 or the medical image storage apparatus 400 in the same manner as in the first embodiment (step S201).

[0065] Subsequently, the specific function 152 executes image analysis using the CT image data acquired by the acquisition function 151 (step S202). Here, the specific function 152 specifies an abnormal region occurring in the blood vessels by performing image analysis based on the pixel values included in the CT image data. For example, the specific function 152 specifies, as an abnormal region, a region where a lesion tissue such as calcium has occurred in an amount exceeding a predetermined amount, a region where an artifact has occurred, a region where the diameter of the blood vessel is smaller than a predetermined threshold value, and the like.

[0066] Subsequently, the specific function 152 specifies a region of interest in the subject's blood vessels based on the analysis result of the image analysis (step S203). For example, the specific function 152 specifies the abnormal region obtained by the image analysis as the region of interest.

[0067] After that, the specific function 152 displays the analysis result of the image analysis and the region of interest on the display 140 (step S204). Here, similar to the first embodiment, the specific function 152 receives, from the operator, an operation to change the region of interest and an operation to instruct whether to perform the analysis of the region of interest via the input circuit 130.

[0068] For example, the specific function 152 displays, on the display 140, a graphic 41 representing the shape of the analysis model, similar to the example shown in FIG. 4. Then, the specific function 152 displays, on the graphic 41 representing the analysis model, information indicating the region determined to be a lesion by the image analysis and information indicating the region where an artifact is determined to have occurred. For example, the specific function 152 colors the region determined to be a lesion by the image analysis and the region where an artifact is determined to have occurred on the graphic 41 representing the analysis model. Further, the specific function 152 displays, on the graphic 41 representing the analysis model, a graphic 42 indicating the range of the specified region of interest. Here, when the specific function 152 receives an operation to change the region of interest, it changes the range of the region of interest according to the received operation and displays, on the graphic 41 representing the analysis model, a graphic 42 representing the changed region of interest.

[0069] When an operation to instruct the specific function 152 to perform the analysis of the region of interest is received (step S205, Yes), the analysis function 153 performs a fluid analysis based on the region of interest set at that time. Here, similar to the first embodiment, the analysis function 153 performs a fluid analysis by simulation using the finite element method.

[0070] Here, in the first fluid analysis, the analysis function 153 performs a fluid analysis with a predetermined accuracy for regions other than the region of interest and performs a fluid analysis with a higher accuracy than the predetermined accuracy for the region of interest.

[0071] At this time, the analysis function 153 performs a fluid analysis with higher accuracy than the predetermined accuracy by increasing the spatial resolution of the image data that forms the basis of the analysis model used in the fluid analysis. Further, the analysis function 153 performs a fluid analysis with higher accuracy than the predetermined accuracy by increasing the calculation cost of the fluid analysis. For example, the analysis function 153 increases the calculation cost of the fluid analysis by increasing the density of the mesh in the analysis model of the finite element method.

[0072] Specifically, similar to the first embodiment, the analysis function 153 requests the X-ray CT apparatus 300 to reconstruct three-dimensional CT image data in which the range where the region of interest is set at that time is expanded, and thereby acquires from the X-ray CT apparatus 300 the CT image data in which the region of interest is expanded and reconstructed (step S206).

[0073] Subsequently, based on the CT image data used in the image analysis performed by the specific function 152, the analysis function 153 generates an analysis model for a region other than the region of interest in the blood vessels included in the CT image data with the aforementioned predetermined accuracy (step S207). Further, based on the CT image data in which the region of interest is expanded and reconstructed, the analysis function 153 generates an analysis model for the region of interest with a higher mesh density than the analysis model generated with the aforementioned predetermined accuracy (step S208).

[0074] Thereafter, the analysis function 153 generates a composite model in which the analysis model for the region other than the region of interest generated with the aforementioned predetermined accuracy and the analysis model for the region of interest generated with higher accuracy than the predetermined accuracy are connected (step S209). By using such a composite model, similar to the first embodiment, for the region of interest, a fluid analysis with higher accuracy is performed compared to the portion other than the region of interest.

[0075] Then, the analysis function 153 executes a fluid analysis of the entire blood vessel using the generated composite model (step S210).

[0076] Thereafter, based on the analysis results obtained by the newly performed fluid analysis, the specific function 152 identifies the region of interest again (step S203), and displays the analysis results of the fluid analysis and the region of interest on the display 140 (step S204).

[0077] Similar to the first embodiment, the operator can determine whether to execute the fluid analysis again by referring to the analysis results of the fluid analysis displayed on the display 140. When an operation instructing the specific function 152 to execute the analysis of the region of interest again is received (step S205, Yes), the analysis function 153 executes the fluid analysis again based on the region of interest set at that time.

[0078] Here, in the fluid analysis after the second time, the analysis function 153 performs a fluid analysis with the same accuracy as the previous fluid analysis for regions other than the region of interest, and performs a fluid analysis with a higher accuracy than the accuracy of the previous fluid analysis for the region of interest.

[0079] At this time, the analysis function 153 performs a fluid analysis with a higher accuracy than the accuracy of the previous fluid analysis by increasing the spatial resolution of the image data that is the basis of the analysis model used in the fluid analysis. Also, the analysis function 153 performs a fluid analysis with a higher accuracy than the accuracy of the previous fluid analysis by increasing the calculation cost of the fluid analysis. For example, the analysis function 153 increases the calculation cost of the fluid analysis by increasing the density of the mesh in the analysis model of the finite element method.

[0080] Specifically, the analysis function 153 acquires, from the X-ray CT apparatus 300, the CT image data obtained by magnifying and reconstructing the region of interest, in the same manner as when performing the first fluid analysis (step S206). Then, the analysis function 153 generates an analysis model for fluid analysis with the aforementioned predetermined accuracy for regions other than the region of interest in the blood vessel, using the CT image data used in the image analysis performed by the specific function 152 (step S207). Further, the analysis function 153 generates an analysis model for the region of interest with a higher mesh density than the analysis model used in the previous fluid analysis, using the acquired CT image data (step S208).

[0081] Thereafter, the analysis function 153 generates a composite model by connecting the analysis model for regions other than the region of interest generated with the aforementioned predetermined accuracy and the analysis model for the region of interest generated with a higher accuracy than the predetermined accuracy (step S209). Then, the analysis function 153 executes a fluid analysis for the entire blood vessel using the generated composite model (step S210).

[0082] In this way, the analysis function 153 repeats the execution of the fluid analysis while an operation instructing the execution of the analysis of the region of interest is received by the specific function 152 (step S205, Yes). When an operation instructing not to execute the analysis of the region of interest is received by the specific function 152 (step S205, No), the analysis function 153 displays the result of the last fluid analysis on the display 140 as the final result (step S211). For example, the analysis function 153 displays the final result in the same manner as the example shown in FIG. 7.

[0083] Note that each of the above steps is realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to each function from the storage circuit 120. Specifically, step S201 is realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to the acquisition function 151 from the storage circuit 120. Further, steps S202 to S205 are realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to the identification function 152 from the storage circuit 120. Further, steps S206 to S211 are realized, for example, when the processing circuit 150 calls and executes a predetermined program corresponding to the analysis function 153 from the storage circuit 120.

[0084] As described above, in the first modification of the first embodiment, the medical image processing apparatus 100 identifies a region of interest in a blood vessel based on the analysis result of image analysis, and performs fluid analysis with higher accuracy on the identified region of interest than on regions other than the region of interest. Therefore, according to this modification, fluid analysis of blood flow can be performed with higher accuracy.

[0085] (Second modification of the first embodiment) Further, in the first modification described above, an example in which the identification function 152 automatically performs image analysis has been described, but the embodiment is not limited to this. For example, the identification function 152 may have a function of performing blood vessel analysis according to an operation received from an operator.

[0086] In this modification, as an analysis related to a blood vessel, the identification function 152 performs blood vessel analysis for analyzing the structure of a blood vessel according to an operation received from an operator, and identifies an abnormal region obtained as an analysis result of the blood vessel analysis as a region of interest.

[0087] Specifically, the identification function 152 extracts information such as a blood vessel center line, a blood vessel wall, and a branching shape from the blood vessels depicted in the CT image data according to an operation received from an operator, and based on the extracted information, identifies a region where stenosis has occurred, a stenosis rate, and the like.

[0088] For example, the specific function 152 identifies the stenosis rate of a blood vessel in response to an operation received from an operator, and specifies a region where the stenosis rate exceeds a predetermined threshold as a region of interest. For example, the specific function 152 specifies a region where the stenosis rate is 50% or more as a region of interest.

[0089] And in this modified example, the analysis function 153 may start fluid analysis while the specific function 152 is specifying the region of interest. In this case, when the region of interest is specified by the specific function 152, the analysis function 153 diverts the results of the fluid analysis that has been completed up to that point for regions other than the region of interest. Thereby, the time required for fluid analysis of the blood vessel can be shortened.

[0090] (Third Modified Example of the First Embodiment) Also, in the first embodiment and the modified examples described above, an example where the analysis function 153 increases the computational cost of fluid analysis by increasing the density of the mesh in the analysis model of the finite element method has been described, but the embodiment is not limited to this.

[0091] For example, the analysis function 153 may increase the computational cost of fluid analysis by changing 1D analysis to 3D analysis. Or, the analysis function 153 may increase the computational cost of fluid analysis by increasing the number of convergence times of the sequential operations performed in fluid analysis.

[0092] (Fourth Modified Example of the First Embodiment) Also, in the first embodiment and the modified examples described above, the analysis function 153 has been configured to generate a composite model by connecting the analysis model for regions other than the region of interest and the analysis model for the region of interest. However, when generating the composite model, the analysis function 153 may smoothly connect the analysis models by performing spatial interpolation on the connection part with the analysis model.

[0093] FIG. 9 is a diagram showing an example of spatial interpolation of an analysis model performed by the analysis function 153 according to the fourth modification of the first embodiment. The plurality of points shown in FIG. 9 each indicate a mesh connection point in the analysis model of the finite element method. Here, the four connection points 91a to 91d shown in the upper part of FIG. 9 indicate the connection points set in the analysis model for the region outside the region of interest. Also, the five connection points 91e to 91i shown in the lower part of FIG. 9 indicate the connection points set in the analysis model for the region of interest.

[0094] For example, as shown in FIG. 9, in the analysis model for the region of interest, since the mesh is set finer compared to the analysis model for the region outside the region of interest, the interval between each connection point becomes narrower. Therefore, when the composite model is generated, due to the partial volume effect or the like, the position of the connection point may shift at the connection part of the analysis model. For example, as shown in FIG. 9, a shift may occur between the connection point 91d located at the end of the connection side of the analysis model for the region outside the region of interest and the connection point 91e located at the end of the connection side of the analysis model for the region of interest.

[0095] In such a case, the analysis function 153 performs spatial interpolation using a method such as spline interpolation at the connection part with the analysis model, thereby smoothly connecting the analysis models. For example, as shown in FIG. 9, instead of the connection point 91d located at the end of the connection side of the analysis model for the region outside the region of interest, the analysis function 153 interpolates a new connection point 91j based on the positions of the other connection points. Thereby, the analysis model for the region of interest and the analysis model for the region outside the region of interest can be smoothly connected, and the accuracy of the fluid analysis using the composite model can be improved.

[0096] (Fifth Modification of the First Embodiment) Also, in the above-described first embodiment and modifications, the analysis function 153 performs fluid analysis using the composite model. However, for the region upstream of the region of interest in the composite model, since the analysis model used in the previous fluid analysis is used, the result of the fluid analysis will not change from the previous time.

[0097] Therefore, when performing fluid analysis using the composite model, the analysis function 153 may omit the calculation of the fluid analysis by using the analysis results obtained from the previous fluid analysis for the region located upstream of the region of interest among the regions other than the region of interest.

[0098] FIG. 10 is a diagram showing an example of fluid analysis performed by the analysis function 153 according to the fifth modification of the first embodiment. The diagrams shown on the left and right sides of FIG. 10 show the composite model 101 of the entire blood vessel, and the dashed arrows indicate the direction of blood flow. Further, in the diagram shown on the right side of FIG. 10, the range indicated by the double arrows indicates the range of the region of interest R in the composite model 101.

[0099] For example, in the example shown in FIG. 10, the analysis function 153 omits the calculation of the fluid analysis by using the analysis results obtained from the previous fluid analysis for the region Ru located upstream of the region of interest R among the regions other than the region of interest R. Further, the analysis function 153 newly performs fluid analysis using the composite model 101 for the region of interest R and the region Rd located downstream of the region of interest R among the regions other than the region of interest. Thereby, the time required for fluid analysis of the blood vessel can be shortened.

[0100] (Sixth Modification of the First Embodiment) Also, in the first embodiment and the modifications described above, the specific function 152 is configured to display the analysis results of the analysis related to the blood vessel on the display 140. However, the specific function 152 may perform both fluid analysis and image analysis, and further display the reliability of the analysis results obtained by the fluid analysis based on the analysis results of the image analysis.

[0101] Specifically, similar to the first embodiment, the specific function 152 performs fluid analysis using the CT image data acquired by the acquisition function 151. Also, similar to the first modification example, the specific function 152 performs image analysis using the CT image data acquired by the acquisition function 151 to identify abnormal regions occurring in blood vessels. For example, the specific function 152 identifies as abnormal regions areas where lesion tissues such as calcium exceed a predetermined amount, areas where artifacts such as motion artifacts and banding artifacts occur, areas where the diameter of blood vessels is smaller than a predetermined threshold value, and the like.

[0102] Then, similar to the first embodiment, the specific function 152 displays the analysis result of the fluid analysis and the region of interest on the display 140. Further, the specific function 152 calculates the reliability of the fluid analysis at each position of the blood vessel based on the analysis value obtained as a result of the image analysis together with the analysis result of the fluid analysis, and displays the calculated reliability on the display 140 together with the analysis result of the fluid analysis.

[0103] For example, the specific function 152 calculates the reliability of the fluid analysis at each position of the blood vessel based on the amount of lesion tissue such as calcium, the amount of artifacts occurring, and the blood vessel diameter. Then, the specific function 152, for example, as shown in FIG. 4, after displaying information indicating the distribution of the values of the blood flow parameters on the graphic 41 representing the shape of the analysis model, further displays information indicating the calculated reliability on the graphic 41. For example, the specific function 152 assigns different colors to each value of the reliability and colors the graphic 41 representing the analysis model according to the spatial distribution of the reliability in the blood vessel. Alternatively, the specific function 152 may display numerical values representing the reliability on the graphic 41 representing the analysis model or in the vicinity of the graphic 41. Thereby, information that can be used as a reference when the operator decides whether to execute the fluid analysis again can be presented.

[0104] (Seventh Modification Example of the First Embodiment) Furthermore, when the specific function 152 includes a branch in a blood vessel that is thicker than a predetermined threshold and the reliability in the region of the branch is lower than a predetermined threshold, a warning may be notified.

[0105] FIG. 11 is a diagram showing an example of a warning display regarding a branch of a blood vessel performed by the specific function 152 according to the seventh modification of the first embodiment. For example, as shown in FIG. 11, the specific function 152 detects a branch 112 that is a blood vessel branching from the blood vessel 111 based on the CT image data acquired by the acquisition function 151, and calculates the blood vessel diameter D of the branch 112 at the location where the detected branch 112 and the blood vessel 111 are connected. Then, for example, when the calculated blood vessel diameter D of the branch 112 is 3 mm or more, the acquisition function 151 detects whether there is a region that includes the connection location between the blood vessel 111 and the branch 112 and where the reliability of the fluid analysis is lower than a predetermined threshold. Then, when there is a corresponding region Rb, the acquisition function 151 displays, on the display 140, the analysis result of the fluid analysis, the information indicating the region Rb, and a warning message.

[0106] Generally, in fluid analysis by simulation, the accuracy of the analysis result may be low when the blood vessel is thin or when the blood vessel is branched. In other words, if the reliability of the analysis result is low even though the blood vessel is thick to a certain extent, there is a possibility that the fluid analysis has not been performed accurately. In this modification, in such a case, the operator can be prompted to perform the fluid analysis again.

[0107] (Eighth Modification of the First Embodiment) In addition, in the first embodiment and the modifications described above, an example of generating an analysis model of a region of interest with increased mesh density based on CT image data obtained by magnifying and reconstructing the region of interest has been described, but the embodiment is not limited to this. For example, instead of acquiring CT image data obtained by magnifying and reconstructing the region of interest, the analysis function 153 may perform image correction on the CT image data and then perform fluid analysis with improved accuracy using the corrected image data.

[0108] In this case, instead of generating an analysis model of the region of interest with increased mesh density based on the CT image data obtained by magnifying and reconstructing the region of interest, the analysis function 153 performs image correction on the CT image data and then generates an analysis model of the region of interest with increased mesh density based on the corrected image data. For example, the analysis function 153 performs predetermined image corrections such as motion artifact correction, banding artifact correction, and phase correction on the CT image data.

[0109] (The ninth modification of the first embodiment) Furthermore, as the above-described image correction, the analysis function 153 may perform image correction selected from among a plurality of types of image corrections instead of performing predetermined image correction. In this case, for example, the content of each image correction is stored in the storage circuit 120 in a database in advance. Examples of the content of the image correction here include correction of the blood vessel wall, correction of pseudo-stenosis, and smoothing of changes in pixel values.

[0110] Then, for example, the analysis function 153 performs the image correction selected from the database by the operator. Alternatively, the analysis function 153 may automatically select an appropriate image correction from the database according to a pattern in which the analysis results of the CT image data and the types of image corrections are associated in advance, and perform the selected image correction according to the analysis results of the image analysis or the fluid analysis. Thereby, the accuracy of the fluid analysis can be improved.

[0111] (The tenth modification of the first embodiment) Also, in the above-described first embodiment and the modifications, an example in which the acquisition function 151 acquires CT image data from the X-ray CT apparatus 300 or the medical image storage apparatus 400 has been described, but the embodiment is not limited thereto. For example, instead of acquiring CT image data, the acquisition function 151 may acquire projection data that is the basis of the CT image data.

[0112] In this case, the acquisition function 151 acquires the projection data serving as the basis of the three-dimensional CT image data including the blood vessels of the subject from the X-ray CT apparatus 300 or the medical image storage apparatus 400, and stores the acquired projection data in the storage circuit 120. Then, when the specifying function 152 and the analyzing function 153 perform analysis on the blood vessels, respectively, they read out the projection data from the storage circuit 120, and reconstruct or magnify and reconstruct the CT image data used for the analysis from the read projection data.

[0113] Alternatively, the acquisition function 151 may acquire the CT image data as in the first embodiment, and the specifying function 152 and the analyzing function 153 may read out the CT image data from the storage circuit 120 and generate the CT image data used for the analysis using the read CT image data. For example, when performing analysis on the blood vessels, the CT image data is converted back to projection data by a method such as forward projection, and the CT image data used for the analysis is reconstructed or magnified and reconstructed from the obtained projection data.

[0114] (11th Modification Example of the First Embodiment) In addition, in the above-described first embodiment and modification examples, an example in which the medical image processing apparatus 100 displays the result of the fluid analysis on the display 140 provided in the apparatus itself has been described, but the embodiment is not limited to this. For example, the medical image processing apparatus 100 may output the analysis result to a medical image display apparatus connected via the network 200.

[0115] In recent years, a medical image processing system may be constructed in the form of a thin client that causes a client device used by an operator to execute only the minimum necessary processing and causes a server device to execute most of the processing. For example, in such a medical image processing system, a processing circuit included in the server device has the same configuration as the medical image processing apparatus 100 described in the above-described first embodiment and the modified example, and a client device (medical image display device) may acquire and display the result of analysis performed by the medical image processing apparatus 100 from the medical image processing apparatus 100. For example, the client device may display the analysis result using a general-purpose browser or the like installed in the device in advance.

[0116] (12th Modification of the First Embodiment) Also, in the above-described first embodiment and the modified example, an example in which CT image data collected by the X-ray CT apparatus 300 is used as image data regarding a subject has been described, but the embodiment is not limited thereto. For example, as the image data regarding the subject, image data collected by another medical image diagnostic apparatus may be used. The medical image diagnostic apparatus here is, for example, an MRI apparatus, an X-ray diagnostic apparatus, an ultrasonic diagnostic apparatus, a PET apparatus, or the like.

[0117] (Second Embodiment) Also, in the above-described first embodiment and the modified example, the case where the medical image processing apparatus 100 executes various processes has been described, but the embodiment is not limited thereto. For example, the above-described various processes may be executed by the X-ray CT apparatus 300. Hereinafter, such an example will be described as the second embodiment.

[0118] FIG. 12 is a diagram showing an example of the configuration of the X-ray CT apparatus 300 according to the second embodiment. For example, as shown in FIG. 1, the X-ray CT apparatus 300 according to the present embodiment includes a gantry 310, a couch 320, and a console 330.

[0119] The gantry 310 is a device that irradiates the subject S (patient) with X-rays, detects the X-rays transmitted through the subject S, and outputs them to the console 330. For example, the gantry 310 includes an X-ray generator 311, an X-ray irradiation control circuit 312, a detector 313, a rotating frame 314, a gantry drive circuit 315, and a data collection circuit 316.

[0120] The X-ray generator 311 generates X-rays and irradiates the generated X-rays onto the subject S. For example, the X-ray generator 311 includes an X-ray tube 311a, a wedge 311b, and a collimator 311c.

[0121] The X-ray tube 311a generates X-rays. For example, the X-ray tube 311a is a vacuum tube and generates X-rays by a high voltage supplied from a high-voltage generator (not shown). Also, the X-ray tube 311a generates X-rays that spread with a fan angle and a cone angle.

[0122] The wedge 311b is an X-ray filter for adjusting the X-ray dose of the X-rays irradiated from the X-ray tube 311a. Specifically, the wedge 311b is a filter that transmits and attenuates the X-rays irradiated from the X-ray tube 311a so that the X-rays irradiated from the X-ray tube 311a to the subject S have a predetermined distribution. For example, the wedge 311b is a filter made by processing aluminum to have a predetermined target angle and a predetermined thickness. Note that the wedge 311b is also called a wedge filter or a bow-tie filter.

[0123] The collimator 311c is a slit for narrowing down the irradiation range of the X-rays whose X-ray dose is adjusted by the wedge 311b under the control of the X-ray irradiation control circuit 312 described later.

[0124] The X-ray irradiation control circuit 312 controls the X-ray generator 311 under the control of a scan control circuit 333, which will be described later. For example, the X-ray irradiation control circuit 312 controls a high-voltage generator (not shown) to supply a high voltage to the X-ray tube 311a included in the X-ray generator 311. Further, the X-ray irradiation control circuit 312 adjusts the X-ray dose irradiated to the subject S by adjusting the tube voltage and tube current supplied to the X-ray tube 311a. Further, the X-ray irradiation control circuit 312 switches the wedge 311b included in the X-ray generator 311. Further, the X-ray irradiation control circuit 312 adjusts the irradiation range (fan angle or cone angle) of the X-rays by adjusting the aperture of the collimator 311c included in the X-ray generator 311.

[0125] The detector 313 detects X-rays generated from the X-ray tube 311a. For example, the detector 313 is a two-dimensional array type detector (area detector) that detects X-rays transmitted through the subject S, and a detection element row in which X-ray detection elements for a plurality of channels are arranged is arranged in a plurality of rows along the body axis direction of the subject S (the Z-axis direction shown in FIG. 1). Specifically, the detector 313 in the present embodiment has X-ray detection elements arranged in multiple rows such as 320 rows along the body axis direction of the subject S, and can detect X-rays transmitted through the subject S over a wide range, such as a range including the lungs and heart of the subject S.

[0126] The rotating frame 314 is a frame formed in an annular shape, and supports the X-ray generator 311 and the detector 313 so as to face each other with the subject S interposed therebetween.

[0127] The gantry drive circuit 315 rotates the rotating frame 314 under the control of a scan control circuit 333, which will be described later, to swing the X-ray generator 311 and the detector 313 on a circular orbit centered on the subject S.

[0128] The data acquisition circuit 316 collects projection data from the detection data of the X-rays detected by the detector 313 under the control of the scan control circuit 333 described later. The data acquisition circuit 316 is also called a DAS (Data Acquisition System). For example, the data acquisition circuit 316 performs amplification processing, A / D conversion processing, sensitivity correction processing between channels, etc. on the X-ray intensity distribution data detected by the detector 313 to generate projection data, and transmits the generated projection data to the console 330 described later. Note that the sensitivity correction processing between channels may be performed by the preprocessing circuit 334 described later.

[0129] The examination table 320 is a device for placing the subject S, and as shown in FIG. 1, it has a top plate 321 on which the subject S is placed and an examination table drive device 322. The examination table drive device 322 moves the top plate 321 in the Z-axis direction to move the subject S into the rotary frame 314.

[0130] The gantry 310 executes, for example, a helical scan in which the rotary frame 314 is rotated while continuously moving the top plate 321 to scan the subject S in a spiral shape. Alternatively, the gantry 310 executes a conventional scan in which the rotary frame 314 is rotated while fixing the position of the subject S after moving the top plate 321 to scan the subject S in a circular orbit. Alternatively, the gantry 310 executes a step-and-shoot method in which a conventional scan is performed at a plurality of imaging positions by moving the position of the top plate 321 at regular intervals.

[0131] The console 330 is a device that accepts the operation of the X-ray CT apparatus 300 by the operator and reconstructs CT image data using the projection data collected by the gantry 310. As shown in FIG. 12, the console 330 has an input circuit 331, a display 332, a scan control circuit 333, a preprocessing circuit 334, a storage circuit 335, an image reconstruction circuit 336, and a processing circuit 337.

[0132] The input circuit 331 has a mouse, a keyboard, a trackball, switches, buttons, a joystick, etc. that are used by the operator of the X-ray CT apparatus 300 to input various instructions and various settings, and transfers the instruction and setting information received from the operator to the processing circuit 337. For example, the input circuit 331 receives from the operator the imaging conditions of the CT image data, the reconstruction conditions when reconstructing the CT image data, the image processing conditions for the CT image data, etc. Further, the input circuit 331 receives a designation operation for designating a predetermined area such as a part on the image or a region of interest.

[0133] The display 332 is a monitor referred to by the operator, and displays the CT image generated from the CT image data to the operator under the control of the processing circuit 337, or displays a GUI (Graphical User Interface) for receiving various instructions and various settings from the operator via the input circuit 331.

[0134] The scan control circuit 333 controls the operation of the X-ray irradiation control circuit 312, the gantry drive circuit 315, the data collection circuit 316, and the bed drive device 322 under the control of the processing circuit 337, thereby controlling the collection process of the projection data in the gantry 310.

[0135] The preprocessing circuit 334 performs a logarithmic conversion process and correction processes such as offset correction, sensitivity correction, and beam hardening correction on the projection data generated by the data collection circuit 316 to generate corrected projection data. Specifically, the preprocessing circuit 334 generates corrected projection data for the projection data generated by the data collection circuit 316 and stores it in the storage circuit 335.

[0136] The storage circuit 335 stores various data. For example, the storage circuit 335 stores the projection data generated by the preprocessing circuit 334 and the CT image data generated by the image reconstruction circuit 336 described later. Further, the storage circuit 335 stores various data generated as a result of the processing executed by the processing circuit 337 described later.

[0137] The image reconstruction circuit 336 reconstructs CT image data using the projection data stored by the memory circuit 335. For example, the image reconstruction circuit 336 reconstructs CT image data by back-projection processing using a method such as the FBP (Filtered Back Projection) method. Alternatively, for example, the image reconstruction circuit 336 reconstructs CT image data using the successive approximation method. Further, the image reconstruction circuit 336 performs various image processes on the CT image data to generate various CT images. Then, the image reconstruction circuit 336 stores the reconstructed CT image data and the CT images generated by various image processes in the memory circuit 335. Note that the image reconstruction circuit 336 is an example of the reconstruction unit in the claims.

[0138] The processing circuit 337 performs overall control of the X-ray CT apparatus 300 by controlling the operations of the gantry 310, the examination table 320, and the console 330. Specifically, the processing circuit 337 controls the CT scan performed by the gantry 310 by controlling the scan control circuit 333. Further, the processing circuit 337 controls the image reconstruction process and the image generation process in the console 330 by controlling the image reconstruction circuit 336. Further, the processing circuit 337 controls the various CT images stored in the memory circuit 335 to be displayed on the display 332.

[0139] Under such a configuration, in the X-ray CT apparatus 300 according to the present embodiment, the processing circuit 337 has an acquisition function 337a, a specification function 337b, and an analysis function 337c. Note that the specification function 337b is an example of the specification unit in the claims. Further, the analysis function 337c is an example of the analysis unit in the claims.

[0140] The acquisition function 337a executes the same processing as the acquisition function 151 described in the first embodiment or the modification example described above. However, in the first embodiment and the modification example described above, the acquisition function 151 acquires CT image data or projection data from the X-ray CT apparatus 300 or the medical image storage apparatus 400, whereas the acquisition function 337a according to the present embodiment acquires CT image data or projection data from the storage circuit 335.

[0141] The specifying function 337b executes the same processing as the specifying function 152 described in the first embodiment or the modification example described above.

[0142] The analysis function 337c executes the same processing as the analysis function 153 described in the first embodiment or the modification example described above.

[0143] Also, in the present embodiment, the input circuit 331, the display 332, and the storage circuit 335 each include the same functions as the input circuit 130, the display 140, and the storage circuit 120 described in the first embodiment described above.

[0144] According to the second embodiment described above, as in the first embodiment, fluid analysis of blood flow can be performed with higher accuracy.

[0145] Note that, in the above-described embodiments, an example in which each processing function is realized by a single processing circuit (processing circuit 150, processing circuit 337) has been described, but the embodiments are not limited thereto. For example, the processing circuit may be configured by combining a plurality of independent processors, and each processor may execute each program to realize each processing function. Further, each processing function of the processing circuit may be appropriately distributed or integrated into a single or a plurality of processing circuits and realized.

[0146] Note that the term "processor" used in the description of each of the above embodiments means, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or a circuit such as 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)). Here, instead of storing a program in a storage circuit, the program may be directly incorporated into the circuit of the processor. In this case, the processor realizes its function by reading and executing the program incorporated in the circuit. Also, each processor of the present embodiment is not limited to being configured as a single circuit for each processor, and a plurality of independent circuits may be combined to be configured as one processor to realize its function.

[0147] Here, the program executed by the processor is provided by being pre-installed in a ROM (Read Only Memory), a storage circuit, or the like. Note that this program may be provided by being recorded on a computer-readable storage medium such as a CD (Compact Disk)-ROM, an FD (Flexible Disk), a CD-R (Recordable), or a DVD (Digital Versatile Disk) in a form installable or executable on these devices. Further, this program may be stored on a computer connected to a network such as the Internet and provided or distributed by being downloaded via the network. For example, this program is composed of modules including each functional unit described later. As actual hardware, the CPU reads the program from a storage medium such as a ROM and executes it, whereby each module is loaded onto the main storage device and generated on the main storage device.

[0148] According to at least one embodiment described above, fluid analysis of blood flow can be performed with higher accuracy.

[0149] Although some embodiments of the present invention 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, replacements, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalent scope thereof.

[0150] Regarding the above embodiments, the following supplementary notes are disclosed as one aspect and optional features of the invention.

[0151] (Supplementary Note 1) An acquisition unit that acquires image data including blood vessels of a subject; an identification unit that identifies a region of interest in the blood vessel based on a first spatial distribution of a blood flow parameter obtained by performing a fluid analysis with a first accuracy using the image data; an analysis unit that updates the first spatial distribution of the blood flow parameters to a second spatial distribution by performing fluid analysis using an analysis result obtained by the fluid analysis with the first accuracy for a region other than the region of interest in the blood vessel that is located upstream of the region of interest, and using a synthetic model that combines an analytical model of the region other than the region of interest generated with the first accuracy and an analytical model of the region of interest generated with a second accuracy higher than the first accuracy for a region other than the region of interest in the blood vessel that is located downstream of the region of interest; A medical image processing device comprising: (Appendix 2) the analysis unit performs the fluid analysis with the first accuracy at a first spatial resolution for a region of the blood vessel other than the region of interest that is located downstream of the region of interest, and performs the fluid analysis with the second accuracy at a second spatial resolution higher than the first spatial resolution for the region of interest in the blood vessel; 2. A medical image processing device according to claim 1. (Appendix 3) the analysis unit performs fluid analysis with a second accuracy higher than the first accuracy by increasing the calculation cost of the fluid analysis. 3. A medical image processing device according to claim 1 or 2. (Appendix 4) the analysis unit performs the fluid analysis with the first accuracy and the fluid analysis with the second accuracy by simulation using a finite element method, and increases the mesh density in the analysis model, thereby increasing the calculation cost. 4. The medical image processing device according to claim 3. (Appendix 5) the analysis unit generates an analytical model in which the density of the mesh is increased, and then corrects the mesh by comparing the analytical model with the image data. 5. A medical image processing device according to claim 4. (Appendix 6) The analysis unit increases the calculation cost by increasing the number of convergence times of the sequential calculation performed in the fluid analysis. The medical image processing apparatus according to Appendix 3. (Appendix 7) The analysis unit generates the composite model by connecting a portion other than the region of interest in the analysis model of the entire blood vessel generated with the first accuracy and the analysis model of the region of interest generated with the second accuracy, and uses the composite model to perform fluid analysis on a region located downstream of the region of interest and the region of interest. The medical image processing apparatus according to any one of Appendices 1 to 6. (Appendix 8) The analysis unit generates the composite model by connecting the analysis model of the region other than the region of interest generated with the first accuracy and the analysis model of the region of interest generated with the second accuracy, and uses the composite model to perform fluid analysis on a region located downstream of the region of interest and the region of interest. The medical image processing apparatus according to any one of Appendices 1 to 6. (Appendix 9) When generating the composite model, the analysis unit smoothly connects the analysis models by performing spatial interpolation on the connection portion between the analysis model with the first accuracy and the analysis model with the second accuracy. The medical image processing apparatus according to Appendix 7 or 8. (Appendix 10) When performing fluid analysis using the composite model, the analysis unit omits the calculation of fluid analysis by using the analysis result obtained by the fluid analysis with the first accuracy for a region located upstream of the region of interest. The medical image processing apparatus according to Appendix 9. (Appendix 11) The specifying unit performs fluid analysis using the image data, and specifies a region where the value of the blood flow parameter obtained as the analysis result of the fluid analysis is outside the normal range as the region of interest. The medical image processing apparatus according to any one of Appendices 1 to 10. (Appendix 12) The specific part performs fluid analysis and image analysis using the image data, and displays the reliability of the analysis result obtained by the fluid analysis based on the analysis result of the image analysis. The medical image processing apparatus according to any one of Appendices 1 to 11. (Appendix 13) When the specific part includes a branch of a blood vessel thicker than a predetermined threshold value in the blood vessel and the reliability in the region of the branch is lower than a predetermined threshold value, a warning is notified. The medical image processing apparatus according to Appendix 12. (Appendix 14) After performing image correction on the image data, the analysis part performs fluid analysis with the second accuracy using the corrected image data. The medical image processing apparatus according to any one of Appendices 1 to 13. (Appendix 15) As the image correction, the analysis part performs image correction selected from among a plurality of types of image corrections. The medical image processing apparatus according to Appendix 14. (Appendix 16) Image data including a blood vessel of a subject is acquired. Based on the first spatial distribution of the blood flow parameters obtained by performing fluid analysis with the first accuracy using the image data, a region of interest in the blood vessel is specified. For the region located upstream of the region of interest among the regions other than the region of interest in the blood vessel, the analysis result obtained by the fluid analysis with the first accuracy is used, and for the region located downstream of the region of interest among the regions other than the region of interest in the blood vessel and the region of interest, the analysis model of the region other than the region of interest generated with the first accuracy and the analysis model of the region of interest generated with a second accuracy higher than the first accuracy are combined. By performing fluid analysis using the combined model, the spatial distribution of the blood flow parameters is obtained, and the first spatial distribution of the blood flow parameters is updated to a second spatial distribution. A medical image processing method including this. (Appendix 17) A reconstructing unit that reconstructs image data including blood vessels of a subject; A specifying unit that specifies a region of interest in the blood vessels based on a first spatial distribution of blood flow parameters obtained by performing fluid analysis with the first accuracy using the image data; For a region located upstream of the region of interest among regions other than the region of interest in the blood vessels, the analysis result obtained by the fluid analysis with the first accuracy is used, and for a region located downstream of the region of interest and the region of interest among regions other than the region of interest in the blood vessels, the analysis result is used. An analysis unit that updates the first spatial distribution of the blood flow parameters to a second spatial distribution by obtaining a spatial distribution of the blood flow parameters by performing fluid analysis using a composite model obtained by combining an analysis model of a region other than the region of interest generated with the first accuracy and an analysis model of the region of interest generated with a second accuracy higher than the first accuracy; An X-ray CT apparatus comprising:

Explanation of Signs

[0152] 100 Medical image processing apparatus 150 Processing circuit 151 Acquisition function 152 Specification function 153 Analysis function

Claims

1. An acquisition unit that acquires image data including a blood vessel of a subject; An analysis unit that obtains a spatial distribution of blood flow parameters in the blood vessel by performing a fluid analysis using the image data; Using the image data, calculates a reliability regarding the fluid analysis by performing an image analysis regarding the blood vessel, detects a branch that is a blood vessel branching from the blood vessel based on the image data, calculates a blood vessel diameter of the branch at a connection location between the branch and the blood vessel, and when the blood vessel diameter is equal to or greater than a predetermined threshold, a specific unit that displays information indicating a region with low reliability of the fluid analysis including the connection location and a warning message A medical image processing apparatus comprising:

2. An acquisition unit that acquires image data including a blood vessel of a subject; An analysis unit that obtains a spatial distribution of blood flow parameters in the blood vessel by performing a fluid analysis using the image data; Using the image data, calculates a reliability regarding the fluid analysis by performing an image analysis regarding the blood vessel, detects a branch that is a blood vessel branching from the blood vessel based on the image data, calculates a blood vessel diameter of the branch at a connection location between the branch and the blood vessel, and for a region where the blood vessel diameter of the branch is equal to or greater than a predetermined threshold and the reliability of the fluid analysis is lower than a predetermined threshold, a specific unit that displays a warning message A medical image processing apparatus comprising:

3. The specific unit displays information indicating a region with low reliability of the fluid analysis and a warning message together with the spatial distribution of the blood flow parameters. The medical image processing apparatus according to claim 1 or 2.

4. The specific unit calculates the reliability of the fluid analysis at each position of the blood vessel based on the amount of calcium. The medical image processing apparatus according to any one of claims 1 to 3.

5. The specific unit calculates the reliability of the fluid analysis at each position of the blood vessel based on the amount of artifacts generated. The medical image processing apparatus according to any one of claims 1 to 3.

6. The specific unit displays the reliability of the fluid analysis on a graphic showing the blood vessel shape. The medical image processing apparatus according to any one of claims 1 to 5. [[ID= ​ ​ ​ The specific part indicates the reliability of the fluid analysis by coloring the graphic showing the blood vessel shape. The medical image processing apparatus according to claim 6.

9. The specific part indicates the reliability of the fluid analysis by displaying a numerical value indicating the reliability on or near the graphic showing the blood vessel shape. The medical image processing apparatus according to claim 6.

10. Obtain image data including a blood vessel of a subject. By performing a fluid analysis using the image data, obtain the spatial distribution of blood flow parameters in the blood vessel. Using the image data, calculate the reliability regarding the fluid analysis by performing image analysis on the blood vessel, detect a branch that is a blood vessel branching from the blood vessel based on the image data, calculate the blood vessel diameter of the branch at the connection location between the branch and the blood vessel, and when the blood vessel diameter is equal to or greater than a predetermined threshold, display information indicating a region with low reliability of the fluid analysis including the connection location and a warning message. A medical image processing method including this.

11. Obtain image data including a blood vessel of a subject. By performing a fluid analysis using the image data, obtain the spatial distribution of blood flow parameters in the blood vessel. Using the image data, calculate the reliability regarding the fluid analysis by performing image analysis on the blood vessel, detect a branch that is a blood vessel branching from the blood vessel based on the image data, calculate the blood vessel diameter of the branch at the connection location between the branch and the blood vessel, and when the blood vessel diameter of the branch is equal to or greater than a predetermined threshold and the reliability of the fluid analysis is lower than a predetermined threshold, display a warning message for a region. A medical image processing method including this.

12. A procedure for obtaining image data including a blood vessel of a subject, A procedure for obtaining the spatial distribution of blood flow parameters in the blood vessel by performing a fluid analysis using the image data, A procedure for calculating the reliability regarding the fluid analysis by performing image analysis on the blood vessel using the image data, detecting a branch that is a blood vessel branching from the blood vessel based on the image data, calculating the blood vessel diameter of the branch at the connection location between the branch and the blood vessel, and when the blood vessel diameter is equal to or greater than a predetermined threshold, displaying information indicating a region with low reliability of the fluid analysis including the connection location and a warning message. A medical image processing program that causes a computer to execute this.

13. A procedure for acquiring image data including blood vessels of a subject, A procedure for obtaining a spatial distribution of blood flow parameters in the blood vessels by performing fluid analysis using the image data, Calculating the reliability of the fluid analysis by performing image analysis on the blood vessels using the image data, detecting a branch that is a blood vessel branching from the blood vessel based on the image data, calculating the blood vessel diameter of the branch at the connection point between the branch and the blood vessel, and displaying a warning message for a region where the blood vessel diameter of the branch is equal to or greater than a predetermined threshold and the reliability of the fluid analysis is lower than a predetermined threshold A medical image processing program that causes a computer to execute.

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