JP2015219371A - Treatment unit, blood vessel model, image processing method, program, and molding device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a blood vessel model corresponding to the material characteristic of the blood vessel of an analyte.SOLUTION: A treatment device 70 comprises a first acquisition unit 70A, a second acquisition unit 70B, a construction unit 70C, and a first generation unit 70D. The first acquisition unit 70A acquires an image pertaining to the blood vessel of an analyte. The second acquisition unit 70B acquires the material characteristic at a prescribed position of the blood vessel. The construction unit 70C constructs, from the image, a shape model indicating the three-dimensional shape of the blood vessel. The first generation unit 70D generates a blood vessel model, which has additional information pertaining to the material characteristic added thereto, at a prescribed position of the blood vessel indicated by the shape model.

Description

  Embodiments described herein relate generally to a processing apparatus, a blood vessel model, an image processing method, a program, and a modeling apparatus.

  A technique is known in which a catheter, a stent graft, or the like is inserted into a blood vessel of a subject to perform diagnosis or treatment. Such treatment requires skillful skills, and it is necessary to accumulate surgical practice in advance.

  Therefore, a technique for modeling a blood vessel virtual simulator and a blood vessel model is disclosed. However, conventionally, virtual simulators and models corresponding to general blood vessel shapes and general blood vessel material characteristics have been provided. For this reason, it was not possible to practice blood vessel surgery according to each subject. That is, conventionally, it has not been possible to provide a blood vessel model according to the material characteristics of each subject's blood vessels.

JP 2011-75907 A

  The problem to be solved by the present invention is to provide a processing device, a blood vessel model, an image processing method, a program, and a modeling device capable of providing a blood vessel model according to the material characteristics of a blood vessel of a subject.

  According to the embodiment, the processing device includes a first acquisition unit, a second acquisition unit, a construction unit, and a first generation unit. The first acquisition unit acquires an image related to the blood vessel of the subject. The second acquisition unit acquires material characteristics at a predetermined position of the blood vessel. The constructing unit constructs a shape model indicating the three-dimensional shape of the blood vessel from the medical image. A 1st production | generation part produces | generates the blood vessel model which added the additional information regarding a material characteristic to the said predetermined position of the blood vessel shown by a shape model.

The hardware block diagram of the medical image diagnostic apparatus of embodiment. The functional block diagram of a processing apparatus. The figure which shows an example of a shape model. Explanatory drawing of blood vessel model generation. Explanatory drawing of blood vessel model generation. Explanatory drawing of blood vessel model generation. The schematic diagram which shows an example of a blood vessel image. The flowchart which shows the procedure of a blood vessel model production | generation process. The flowchart which shows the procedure of the modeling process of a blood vessel model. The flowchart which shows the procedure of display control. The block diagram which shows the hardware structural example. The figure which shows an example of a dynamic model. The figure which shows the flow of a structural fluid analysis process. The functional block diagram of an analyzer. The figure which shows typically the cross section orthogonal to the core line of the blood vessel. The figure which shows the time change of the blood-vessel core form. The figure which shows an example of a tracking process. The figure which shows the example of calculation of the bending deformation of a blood-vessel core wire, and rotational displacement. The figure which shows the cross section orthogonal to the core wire of a shape model. Explanatory drawing of assignment of the forced displacement history to a shape model. Explanatory drawing of assignment of the forced displacement history to a shape model. Explanatory drawing of other allocation of the forced displacement log | history to a shape model. Explanatory drawing of posterior distribution calculation and identification of average internal pressure. Explanatory drawing of posterior distribution calculation and material model parameter identification. The figure which shows the example of a display of a dynamic parameter | index. The figure which shows the example of a display of a blood flow parameter | index. The figure which shows the example of a graph regarding the blood pressure of the left coronary artery origin part. The figure which shows the example of a display of the graph regarding the blood pressure near a branch point. The figure which shows the example of a display of the graph regarding the blood pressure change regarding a core line direction. The figure which shows the other example of allocation of a forced displacement log | history. The figure which shows the other example of allocation of a forced displacement log | history. The figure which shows the other example of allocation of a forced displacement log | history. The figure which shows the deformation | transformation of a fiber group. The figure which shows the orthogonal cross section of a dynamic model. It is an enlarged view of a minute sector element.

  Exemplary embodiments of a processing apparatus, a blood vessel model, an image processing method, a program, and a modeling apparatus will be described below in detail with reference to the accompanying drawings.

  In the embodiment, as an example, a case will be described in which the processing device is applied to a computer device for structural fluid analysis of an image generated by a medical image diagnostic device. In the present embodiment, as an example, a case where the medical image diagnostic apparatus analyzes a medical image will be described. This computer apparatus may be incorporated in the medical image diagnostic apparatus, or may be a workstation or the like separate from the medical image diagnostic apparatus. Hereinafter, a medical image diagnostic apparatus incorporating a computer apparatus to which a processing apparatus according to an embodiment is applied will be described in detail with reference to the drawings. The processing device may be configured as a separate body from the medical image diagnostic device.

  The medical image diagnostic apparatus of the embodiment can be applied to any type of image diagnostic apparatus equipped with an imaging mechanism for scanning a subject. Examples of the medical image diagnostic apparatus according to the embodiment include an X-ray computed tomography apparatus (X-ray CT apparatus), a magnetic resonance diagnostic apparatus, an ultrasonic diagnostic apparatus, a SPECT (Single Photon Emission CT) apparatus, and a PET (Positron Emission Tomography). The present invention can be appropriately used for an apparatus, a radiotherapy apparatus, and the like. Hereinafter, the medical image diagnostic apparatus according to the embodiment is assumed to be an X-ray computed tomography apparatus for specific description.

  FIG. 1 is a schematic hardware configuration diagram of a medical image diagnostic apparatus (X-ray computed tomography apparatus) according to an embodiment. As shown in FIG. 1, the X-ray computed tomography apparatus includes a CT gantry 10, a console 20, and a modeling unit 72. The CT mount 10 and the modeling unit 72 are connected to the console 20 so as to be able to exchange signals.

  The modeling unit 72 is a known device for manufacturing a three-dimensional modeled object. The modeling unit 72 may be an apparatus capable of modeling a three-dimensional modeled object, and may be, for example, either a hot melt lamination method or a powder fixing method.

  In the present embodiment, the material used by the modeling unit 72 for modeling is preferably a material that satisfies the range that can be taken by the mechanical characteristics of human blood vessels. Specifically, the material used for modeling by the modeling unit 72 is preferably a material having a Young's modulus of about 0.01 MPa to 20 MPa. For example, the modeling part 72 is preferably modeled using a simulated blood vessel material having flexibility and elasticity such as silicone rubber.

  The CT gantry 10 scans the imaging region of the subject with X-rays under the control of the gantry controller 23 of the console 20. The imaging site is, for example, the heart.

  The CT mount 10 includes an X-ray tube 11, an X-ray detector 13, and a data acquisition device 15. The X-ray tube 11 and the X-ray detector 13 are mounted on the CT mount 10 so as to be rotatable around the rotation axis Z. The X-ray tube 11 irradiates a subject into which a contrast medium has been injected with X-rays. The X-ray detector 13 detects X-rays generated from the X-ray tube 11 and transmitted through the subject, and generates an electric signal corresponding to the detected X-ray intensity.

  The data collection device 15 reads an electrical signal from the X-ray detector 13 and converts it into digital data. A set of digital data for each view is called a raw data set. A time-series raw data set relating to a plurality of scan times is transmitted to the console 20 by a non-contact data transmission device (not shown).

  The console 20 includes a gantry control unit 23, a reconstruction device 25, a blood vessel analysis device 50, a processing device 70, an input unit 29, a display unit 31, and a storage unit 33.

  The gantry control unit 23 controls each device in the console 20 according to the scanning conditions set by the user via the input unit 29.

  The reconstruction device 25 generates a CT image related to the subject based on the raw data set. Specifically, first, the reconstruction device 25 generates a projection data set by pre-processing the raw data set. Pre-processing includes logarithmic conversion, nonuniformity correction, calibration correction, and the like. Next, the reconstruction device 25 performs an image reconstruction process on the projection data set to generate a CT image. As an image reconstruction algorithm, an analytical image reconstruction method such as FBP (filtered back projection) method, an ML-EM (maximum likelihood projection optimization) method, an OS-EM (ordered subset extraction approximate image) method, etc. Existing algorithms such as reconstruction can be employed.

  In the embodiment, the reconstruction device 25 generates a time-series CT image based on the time-series projection data set. The CT image includes a pixel region (hereinafter referred to as a blood vessel region) related to a blood vessel contrasted with a contrast agent. Note that the CT image may be slice data representing a two-dimensional spatial distribution of CT values, or volume data representing a three-dimensional spatial distribution of CT values. Hereinafter, it is assumed that the CT image is volume data. A time-series CT image is stored in the storage unit 33.

  The blood vessel analysis device 50 includes a system control unit 21 and an analysis device 27.

  The system control unit 21 is connected to the gantry control unit 23, the reconstruction device 25, the analysis device 27, the input unit 29, the display unit 31, and the storage unit 33, and controls them.

  The analysis device 27 performs structural fluid analysis using the image. In the present embodiment, as an example, the analysis device 27 will be described using a time-series CT image (medical image) to perform structural fluid analysis. Then, the analysis device 27 calculates material characteristics (details will be described later) at a predetermined position of the blood vessel of the subject. Specifically, the analysis device 27 calculates a material characteristic by analyzing a mechanical model that is finally constructed from a medical image of the subject by a process described later. In the present embodiment, as an example, the analysis device 27 will be described with respect to a case where the material property at each position of the blood vessel of the subject is calculated. Details of the analysis device 27 will be described later.

  The medical image is an image that can identify the shape of the blood vessel of the subject. The medical image is, for example, a time-series CT image, an MRI image, an ultrasonic echo image, or the like.

  In the present embodiment, a case where a time-series CT image is used as a medical image will be described as an example.

  The blood vessel analyzing apparatus 50 may be incorporated in a medical image diagnostic apparatus (X-ray computed tomography apparatus), or may be a computer apparatus separate from the medical image diagnostic apparatus. When the blood vessel analysis device 50 is separate from the medical image diagnostic device, the blood vessel analysis device 50 collects medical images such as time-series CT images from the medical image diagnostic device or PACS (picture learning and communication systems) via the network. Just do it.

  The storage unit 33 includes various storage media such as a hard disk device. The storage unit 33 stores various data such as time-series projection data and time-series CT images. For example, the storage unit 33 stores time-series CT images in a medical image file format conforming to the DICOM (digital imaging and communications in medicine) standard. The storage unit 33 may store medical images collected by an external device.

  The input unit 29 receives various instructions and information input from the user. The input unit 29 is, for example, a keyboard, a mouse, a switch, or the like.

  The display unit 31 displays various images. The display unit 31 is, for example, a CRT display, a liquid crystal display, an organic EL display, a plasma display, or the like.

  Note that the UI unit 73 in which the input unit 29 and the display unit 31 are integrally constructed may be used. The UI unit 73 includes a touch panel having both a display function and an input function.

  The processing device 70 is electrically connected to the analysis device 27, the reconstruction device 25, the input unit 29, the display unit 31, and the storage unit 33. Further, the processing device 70 is electrically connected to the modeling unit 72.

  The processing device 70 is a device that generates a blood vessel model.

  The blood vessel model is image data used for generating a blood vessel model of a blood vessel of a subject and image data used for generating a blood vessel image of a blood vessel of the subject.

  FIG. 2 is a functional block diagram of the processing device 70.

  The processing device 70 includes a first acquisition unit 70A, a second acquisition unit 70B, a construction unit 70C, a first generation unit 70D, a first control unit 70E, a reception unit 70F, a second generation unit 70G, A second control unit 70H and a third control unit 70I are included.

  First acquisition unit 70A, second acquisition unit 70B, construction unit 70C, first generation unit 70D, first control unit 70E, reception unit 70F, second generation unit 70G, second control unit 70H, and third control unit 70I A part or all of, for example, causes a processing device such as a CPU (Central Processing Unit) to execute a program, that is, may be realized by software, or may be realized by hardware such as IC (Integrated Circuit). It may be realized by using software and hardware together.

  70 A of 1st acquisition parts acquire the image regarding the blood vessel of a subject. 70 A of 1st acquisition parts acquire the image regarding the blood vessel of a subject from the reconstruction apparatus 25 (refer FIG. 1). The first acquisition unit 70A may acquire an image related to the blood vessel of the subject from the storage unit 33.

  The second acquisition unit 70B acquires material characteristics of a predetermined position of the blood vessel of the subject in the medical image. The second acquisition unit 70B acquires material characteristics at a predetermined position of the blood vessel of the subject from the analysis device 27. In the present embodiment, a case will be described in which the second acquisition unit 70B acquires material properties at each position of the blood vessel of the subject from the analysis device 27.

  The material property indicates the blood vessel deformation rate with respect to the pressure of the blood vessel of the subject. Specifically, the material properties indicate the elastic modulus, flexibility, strength, hardness (hardness), toughness, and the like of each position along the core line direction and the circumferential direction of the blood vessel of the subject.

  The elastic modulus is, for example, Young's modulus or Poisson's ratio. The strength indicates, for example, rigidity, tensile strength, compressive strength, shear strength, and the like. The hardness indicates, for example, Vickers hardness, Brinell hardness, Rockwell hardness, Shore hardness, Knoop hardness, Mohs hardness, and the like. Toughness refers to the work load, tenacity, brittleness (brittleness), etc. for material destruction.

  Note that the second acquisition unit 70B may acquire a dynamic model from the analysis device 27. Then, the material property of the blood vessel of the subject may be acquired by analyzing the dynamic model.

  A dynamic model is a shape model that shows a three-dimensional shape of a blood vessel constructed from time-series medical images, a blood vessel shape history (time-series blood vessel shape index), and a forced displacement history (time-series blood vessel shape deformation index). ) To which the blood vessel deformation behavior, which is difficult to derive directly from the medical image, is added.

  The construction unit 70C constructs a shape model from the time-series medical images of the subject acquired by the first acquisition unit 70A. The shape model is data indicating the three-dimensional shape of the blood vessel of the subject. Specifically, the shape model is data indicating the geometric structure of the blood vessel at each time. A known method may be used to construct the shape model.

  FIG. 3 is a diagram illustrating an example of the shape model 80. As shown in FIG. 3A, the shape model 80 is data indicating the three-dimensional shape itself of the blood vessel of the subject, and is constructed from a medical image. For this reason, the shape model 80 does not include material characteristics such as the elastic modulus of the actual blood vessel of the subject.

  Actually, as shown in FIG. 3B, the actual material characteristics of the blood vessel of the subject corresponding to the blood vessel having the shape shown by the shape model 80 differ depending on each position in the blood vessel. For example, as shown in FIG. 3B, among the blood vessel wall 82 of the blood vessel having the shape shown by the shape model 80, for example, the position A1 where the plaque is formed has high rigidity, and the position A3 where the plaque is not formed. The rigidity is low in the order of the position A2 continuous with the plaque. However, since the shape model 80 constructed from the medical image shows the shape itself, the material characteristics of the blood vessel of the actual subject cannot be obtained from the shape model 80.

  Therefore, the first generation unit 70D adds additional information related to the material characteristics of the blood vessel of the subject acquired by the second acquisition unit 70B to a predetermined position of the blood vessel of the subject indicated by the shape model constructed by the construction unit 70C. An added blood vessel model is generated. In the present embodiment, as an example, the first generation unit 70D generates a blood vessel model in which additional information regarding material characteristics corresponding to each position is added to each position of the blood vessel of the subject indicated by the shape model. Will be explained.

  The blood vessel model is obtained by adding additional information related to material characteristics at each position of the blood vessel to the shape model which is data indicating the three-dimensional shape of the blood vessel of the subject.

  The additional information may be information regarding material characteristics for each position of the blood vessel indicated by the shape model. The additional information may be material characteristics themselves, information indicating means and methods that can realize the material characteristics, or information obtained by converting the material characteristics into different types of characteristics.

  Further, the additional information only needs to indicate information on the material characteristics of the corresponding position in the blood vessel of the subject for each three-dimensional coordinate of each pixel constituting the shape model.

  The additional information regarding the material characteristics includes, for example, information obtained by correcting the relative position of the outer wall with respect to the inner wall of the blood vessel wall, information indicating the type of modeling material at each position of the blood vessel model according to the blood vessel model, and the application position of the reinforcing member. At least one piece of information to be shown. Any of these additional information is generated for each position of the blood vessel according to the material characteristics.

  First, a case where the additional information is information obtained by correcting the relative position of the outer wall with respect to the inner wall of the blood vessel wall will be described. For example, the first generation unit 70D applies the inner wall of the blood vessel indicated by the blood vessel model so that the thickness of the blood vessel wall indicated by the blood vessel model to be generated becomes a thickness according to the material characteristics of each position of the blood vessel of the subject A blood vessel model is generated with additional information obtained by correcting the relative position of the outer wall.

  FIG. 4 is an explanatory diagram of blood vessel model generation. FIG. 4A is a diagram illustrating an example of the shape model 40. FIG. 4B is an explanatory diagram of an example of a blood vessel model M102 in which additional information 48 is added to the shape model 40 shown in FIG.

  For example, it is assumed that the hardness as the material characteristic of the blood vessel of the subject acquired by the second acquisition unit 70B indicates a relationship of position A1> position A3> position A2 in the blood vessel as shown in FIG. That is, it is assumed that the hardness at the position A1 is the highest, and the hardness is low in this order of the position A3 and the position A3.

  In this case, as shown in FIG. 4B, the first generation unit 70D determines that the thickness of the blood vessel wall 42 indicated by the blood vessel model M102 to be generated is the material property of each position A1 to position A3 of the blood vessel of the subject. A blood vessel to which additional information (see additional information 48A to 48C in FIG. 4B) is added to correct the relative position of the outer wall 40B to the inner wall 40A of the blood vessel indicated by the blood vessel model M102 so as to achieve a realizable thickness. A model M102 is generated.

  Specifically, when the material property indicates hardness, the first generation unit 70D corrects the relative position of the outer wall 40B with respect to the inner wall 40A of the blood vessel so that the thickness of the blood vessel wall 42 increases as the hardness increases. Add information. Similarly, when the material property indicates the elastic modulus, the first generation unit 70D adds the corrected relative position of the outer wall 40B to the inner wall 40A of the blood vessel so that the lower the elastic modulus, the thicker the blood vessel wall 42 becomes. Add information. Similarly, when the material property indicates flexibility, the first generation unit 70D corrects the relative position of the outer wall 40B with respect to the inner wall 40A of the blood vessel so that the thickness of the blood vessel wall 42 increases as the flexibility decreases. Append additional information. In addition, when the material property indicates strength, the first generation unit 70D adds additional information obtained by correcting the relative position of the outer wall 40B with respect to the inner wall 40A of the blood vessel so that the thickness of the blood vessel wall 42 increases as the strength increases. To do.

  In addition, when the first generation unit 70D indicates a characteristic in which a plurality of parameters indicating material characteristics such as elastic modulus, flexibility, strength, hardness, and toughness are combined, the first generation unit 70D Additional information in which the relative position of the outer wall 40B with respect to the inner wall 40A of the blood vessel is corrected is added so that the material property can be realized.

  In other words, the first generation unit 70D does not change the inner diameter of the blood vessel of the subject indicated by the shape model, but changes the outer diameter of the blood vessel (that is, changes the position of the outer wall), so that the inner wall 40A of the blood vessel is changed. Additional information 48 (see additional information 48A to 48C) in which the relative position of the outer wall 40B with respect to is corrected is added.

  Therefore, as shown in FIG. 4B, the blood vessel model M102 has substantially the same inner diameter as the inner diameter indicated by the shape model 40, as shown in FIG. On the other hand, the outer diameter of the blood vessel indicated by the blood vessel model M102 is different from the outer diameter of the blood vessel indicated by the shape model 40. That is, the blood vessel model M102 is obtained by correcting the relative position of the outer wall 40B with respect to the inner wall 40A so as to have a thickness capable of realizing the material characteristics at the corresponding position.

  Next, a case where the additional information is information indicating the type of modeling material at each position of the blood vessel model corresponding to the blood vessel model will be described.

  For example, the first generation unit 70D indicates additional information indicating the type of modeling material at each position of the blood vessel model so that the modeling material at each position of the blood vessel model corresponding to the blood vessel model satisfies the material characteristics of each corresponding position. A blood vessel model to which is added is generated.

  FIG. 5 is an explanatory diagram of blood vessel model generation. FIG. 5A shows an example of the shape model 40, which is the same as FIG. 4A. Similarly to FIG. 4A, for example, the hardness as the material characteristic of the blood vessel of the subject acquired by the second acquisition unit 70B is such that position A1> position A3> position A2, as shown in FIG. Let's show the relationship. That is, it is assumed that the hardness at the position A1 is the highest, and the hardness is low in this order of the position A3 and the position A3.

  FIG. 5B is an explanatory diagram of an example of a blood vessel model M100 in which additional information 46 is added to the shape model 40 shown in FIG.

  In this case, as shown in FIG. 5B, the first generation unit 70D uses the modeling material of the blood vessel wall 42 indicated by the blood vessel model D100 corresponding to the blood vessel model M100 to be generated, for each position A1 of the blood vessel of the subject. A blood vessel model M100 is generated to which additional information 46 (additional information 46A to 46C) indicating the type of modeling material at each position of the blood vessel model D100 is added so as to satisfy the material characteristics of the position A3.

  Specifically, when the material property indicates hardness, the first generation unit 70D is a material that can realize the hardness at each position indicated by the material property, and the modeling unit 72 (models the blood vessel model D100). 1), information indicating the type of modeling material that can be used is added. Similarly, the first generation unit 70D is a material that can realize the elastic modulus at each position indicated by the material characteristic when the material characteristic indicates the elastic modulus, and the modeling unit 72 (models the blood vessel model D100). Additional information indicating the type of modeling material that can be used in FIG. 1 is added. Similarly, when the material property indicates flexibility, the first generation unit 70D is a material that can realize the flexibility at each position indicated by the material property, and forms the blood vessel model D100. Additional information indicating the type of modeling material that can be used in the modeling unit 72 (see FIG. 1) is added. In addition, when the material property indicates strength, the first generation unit 70D is a material that can realize the strength at each position indicated by the material property, and the modeling unit 72 that models the blood vessel model D100 (see FIG. 1). ) To add information indicating the type of modeling material that can be used.

  In addition, when the first generation unit 70D indicates a characteristic in which a plurality of parameters indicating material characteristics such as elastic modulus, flexibility, strength, hardness, and toughness are combined, the first generation unit 70D Additional information indicating the type of modeling material corresponding to the material characteristics is added.

  That is, the first generation unit 70D does not change the shape of the blood vessel of the subject indicated by the shape model in order to realize the mechanical strength according to the material characteristics, and changes the type of modeling material used for modeling the blood vessel model D100. Additional information determined for each position is added.

  For this reason, as shown in FIG. 5B, the blood vessel model M100 is substantially the same as the shape and size indicated by the shape model 40 with respect to the inner diameter, outer diameter, and shape of the blood vessel. However, in the blood vessel model M100, the type of modeling material capable of realizing the material characteristics at the corresponding position in the shape model 40 is given as additional information.

  Next, a case where the additional information is information indicating the application position of the reinforcing member will be described.

  For example, the first generation unit 70D adds the additional information indicating the application position of the reinforcing member so that the mechanical characteristics at each position of the blood vessel model corresponding to the blood vessel model satisfy the material characteristics at each corresponding position. Is generated.

  FIG. 6 is an explanatory diagram of blood vessel model generation. FIG. 6A shows an example of the shape model 40, which is the same as FIG. 4A. As in FIG. 4A, for example, the hardness as the material characteristic of the blood vessel of the subject acquired by the second acquisition unit 70B is such that position A1> position A3> position A2, as shown in FIG. Let's show the relationship. That is, it is assumed that the hardness at the position A1 is the highest, and the hardness is low in this order of the position A3 and the position A3.

  FIG. 6B is an explanatory diagram of an example of a blood vessel model M104 in which additional information 49 is added to the shape model 40 shown in FIG.

  In this case, as shown in FIG. 6B, the first generation unit 70D has the mechanical strength at each position of the blood vessel model D104 corresponding to the blood vessel model M104 to be generated as the positions A1 to A3 of the blood vessels of the subject. The additional information 49 (additional information 49A to 49C) indicating the type of the reinforcing member P that reinforces the blood vessel indicated by the blood vessel model D104 from the outside and the applying position of the reinforcing member P is added so as to satisfy the material characteristics of A blood vessel model M104 is generated.

  More specifically, as shown in FIG. 6B, the first generation unit 70D has a strength when measured from the inner wall side of the blood vessel wall 42 in the blood vessel model D104 corresponding to the blood vessel model M104 to be generated. A blood vessel to which additional information 49 (additional information 49A to 49C) indicating the type of the reinforcing member P and the position where the reinforcing member P is applied to the outside of the blood vessel wall 42 is added so as to satisfy the material characteristics of each position. A model M104 is generated.

  Specifically, when the material property indicates hardness, the first generation unit 70D determines the type (for example, thickness) of the reinforcing member P so that the thicker reinforcing member P is installed at a higher position. A blood vessel model M104 is generated to which additional information indicating the position where the reinforcing member P is applied to the outside of the blood vessel wall 42 is added. Similarly, when the material property indicates the elastic modulus, the first generation unit 70D determines the type of the reinforcing member P and the vascular wall 42 so that the thicker reinforcing member P is installed at a position where the elastic modulus is lower. A blood vessel model M104 is generated to which additional information indicating the application position of the reinforcing member P to the outside is added. Similarly, when the material property indicates flexibility, the first generation unit 70D determines the type of the reinforcing member P and the blood vessel wall so that the thicker reinforcing member P is installed at a position where the flexibility is lower. A blood vessel model M104 to which additional information indicating the position where the reinforcing member P is applied to the outside of 42 is added is generated. Similarly, when the material property indicates strength, the first generation unit 70D moves the type of the reinforcing member P and the outside of the blood vessel wall 42 so that the thicker reinforcing member P is installed at a higher strength position. A blood vessel model M104 is generated to which additional information indicating the application position of the reinforcing member P is added.

  The additional information 49 may be information indicating at least the position where the reinforcing member P is applied, and is not limited to a form including both the position where the reinforcing member P is applied and the type of the reinforcing member P. Further, the type of the reinforcing member P indicates, for example, thickness, material, shape, and the like.

  In addition, when the first generation unit 70D indicates a characteristic obtained by combining a plurality of parameters indicating material characteristics such as elastic modulus, flexibility, strength, hardness, and toughness, the plurality of parameters The additional information indicating the type of the reinforcing member P and the applying position of the reinforcing member P is added so that the reinforcing member P having a thickness or material corresponding to the material characteristic is applied to the position corresponding to the material characteristic. A blood vessel model M104 is generated.

  In the example shown in FIG. 6B, the first generation unit 70D installs the reinforcing member P2 and the reinforcing member P3 in the blood vessel indicated by the blood vessel model D104 at a position corresponding to the blood vessel position A3 of the subject. The additional information indicating that is added. The material of the reinforcing member P2 and the reinforcing member P3 is the same, and the thickness is also the same. Further, the first generation unit 70D adds additional information indicating that the reinforcing member P1 thicker than the reinforcing member P2 is installed at a position corresponding to the blood vessel position A1 of the subject in the blood vessel indicated by the blood vessel model D104. Is added. Then, the first generation unit 70D adds additional information indicating that no reinforcing member P is installed at the position of the blood vessel indicated by the blood vessel model D104 corresponding to the blood vessel position A2 of the subject.

  In addition, the reinforcing member P installed in each position is comprised with the same material, and it is good also as a material characteristic realizable by adjusting thickness. In addition, the reinforcing members P installed at the respective positions have substantially the same thickness, and may be configured with different materials capable of realizing material characteristics for each position, or may be configured with different materials for each position. .

  Returning to FIG. 2, the first control unit 70 </ b> E displays the blood vessel image indicated by the shape model constructed by the construction unit 70 </ b> C on the display unit 31. FIG. 7 is a schematic diagram illustrating an example of the blood vessel image 90.

  Returning to FIG. 2, the accepting unit 70F accepts an instruction from the user. Specifically, the user instructs a specific position in the blood vessel image 90 by operating the input unit 29 while referring to the blood vessel image 90 displayed on the display unit 31. Then, the accepting unit 70F accepts position information indicating the position instructed by the user in the blood vessel image 90 as an instruction by the user.

  The second generation unit 70G generates an output signal having an intensity corresponding to the material characteristic indicated by the additional information corresponding to the position designated by the user in the displayed blood vessel image 90.

  For example, the second generation unit 70G reads the material characteristic indicated by the additional information corresponding to the position designated by the user in the displayed blood vessel image 90 from the blood vessel model generated by the first generation unit 70D. Then, the second generation unit 70G generates an output signal having an intensity corresponding to the read material characteristic.

  For example, when the read material property indicates the elastic modulus, the second generation unit 70G generates an output signal having a higher strength (for example, an output signal having a higher voltage value) as the elastic modulus is lower. For example, when the read material property indicates hardness, the second generation unit 70G generates an output signal having a higher strength (for example, an output signal having a higher voltage value) as the hardness is higher. For example, when the read mechanical characteristic indicates toughness, the second generation unit 70G generates an output signal having a higher strength as the toughness is higher.

  Then, the second control unit 70H controls the input unit 29 so as to generate a stress corresponding to the intensity of the output signal generated by the second generation unit 70G.

  In the present embodiment, the stress indicates an internal force per unit area that is generated in a predetermined region operated by the user in the input unit 29 when a predetermined load is received by the user. For example, the second control unit 70H controls the input unit 29 so that the stress increases as the intensity of the output signal generated by the second generation unit 70G increases.

  For example, it is assumed that a position on the blood vessel image 90 is indicated by operating the position of the pointer P1 displayed on the blood vessel image 90 illustrated in FIG. 7 according to an operation instruction of the input unit 29 by the user. At this time, a user's hand or the like who operates the input unit 29 can transmit stronger stress as the material property corresponding to the position is higher. Specifically, the stress corresponding to the material characteristic at the position of the pointer P1 on the blood vessel image 90 can be transmitted to a portion (for example, a mouse button portion) grasped by the user in the input unit 29.

  For this reason, as the user operates the input unit 29 to indicate a position with high mechanical strength indicated by the material characteristics in the blood vessel image 90, the stress at the input unit 29 increases. For this reason, the user feels a stronger stress as he / she points to a position having a higher mechanical strength indicated by the material characteristics in the blood vessel image 90. For this reason, the user can grasp the state of the blood vessel corresponding to the actual material characteristic of the blood vessel of the subject by instructing the blood vessel image 90 on the screen.

  Returning to FIG. 2, the third control unit 70 </ b> I controls the modeling unit 72 to model a blood vessel model corresponding to the blood vessel model.

  For example, assume that the blood vessel model is a blood vessel model M102 shown in FIG.

  In this case, the third control unit 70I controls the modeling unit 72 so as to model the blood vessel model D102 having the shape indicated by the blood vessel model M102. The modeling unit 72 models a blood vessel model D102 corresponding to the blood vessel model M102. Therefore, as shown in FIG. 4B, the modeling unit 72 models a blood vessel model D102 that is substantially the same as the inner diameter indicated by the shape model 40 with respect to the inner diameter of the blood vessel. However, the modeling unit 72 models the blood vessel model D102 in which the thickness of the blood vessel wall of the blood vessel model D102 is corrected to a thickness that can realize the material characteristics at the corresponding position.

  That is, the blood vessel model D102 is a blood vessel model D102 imitating the blood vessel of the subject, and has an inner diameter corresponding to the blood vessel of the subject, and the relative position of the outer wall with respect to the inner wall of the blood vessel is a material characteristic of the blood vessel of the subject. This is a blood vessel model in which the position can be realized.

  Further, for example, it is assumed that the blood vessel model is a blood vessel model M100 shown in FIG.

  In this case, the third control unit 70I causes the modeling unit 72 to model the blood vessel model D100 with a modeling material indicated by the blood vessel model M100 and the type of modeling material indicated by the additional information 46 of the blood vessel model M100. Control. For this reason, as shown in FIG. 5 (B), the modeling portion 72 is substantially the same as the shape and size indicated by the shape model 40 with respect to the inner diameter, outer diameter, and shape of the blood vessel. A blood vessel model D100 formed with a type of modeling material capable of realizing the characteristics is formed.

  At this time, after modeling the blood vessel model with the same material, the modeling unit 72 uses light or heat so that the characteristics of the type of modeling material indicated by the additional information 46 are expressed at the position indicated by the additional information 46. The material may be modified by applying a stimulus such as a blood vessel model D100.

  Further, for example, it is assumed that the blood vessel model is a blood vessel model M104 shown in FIG.

  In this case, the third control unit 70I applies the blood vessel model D104 provided with the reinforcing member P indicated by the additional information to the application position indicated by the additional information outside the blood vessel in the blood vessel model indicated by the shape model 40. The modeling unit 72 is controlled so as to model.

  In this case, it is preferable that the third control unit 70I controls the modeling unit 72 to use the above-described simulated blood vessel material for the shape portion indicated by the shape model 40. On the other hand, the portion of the reinforcing member P may be formed with a material other than the material satisfying the above-described characteristics of the simulated blood vessel material. Further, the third control unit 70I controls the modeling unit 72 to model the blood vessel model indicated by the shape model 40, and displays the application information indicating the type of the reinforcing member P and the application position of the reinforcing member P. You may display on the part 31. In this case, the user may model the blood vessel model D104 by installing the reinforcing member P indicated by the application information displayed on the display unit 31 on the model modeled by the modeling unit 72.

  Next, an image processing procedure executed by the processing device 70 will be described.

  FIG. 8 is a flowchart illustrating a procedure of blood vessel model generation processing executed by the processing device 70.

  First, the first acquisition unit 70A acquires a medical image related to the blood vessel of the subject (step S100). Next, the construction unit 70C constructs a shape model indicating the three-dimensional shape of the blood vessel from the medical image acquired in step S100 (step S102).

  Next, the second acquisition unit 70B acquires material characteristics at each position of the blood vessel (step S104). Next, the first generation unit 70D generates a blood vessel model in which additional information regarding the material properties acquired in step S104 is added to each position of the blood vessel indicated by the shape model constructed in step S102 (step S106).

  Next, the first generation unit 70D associates the blood vessel model generated in step S106 with the shape model constructed in step S102 and the identification information of the subject, and stores them in the storage unit 33 (see FIG. 1). (Step S108). Then, this routine ends.

  FIG. 9 is a flowchart showing the procedure of the blood vessel model modeling process.

  First, the receiving unit 70F determines whether or not a modeling instruction has been received from the input unit 29 (step S200). If a negative determination is made in step S200 (step S200: No), this routine ends. On the other hand, if an affirmative determination is made in step S200 (step S200: Yes), the process proceeds to step S202.

  In step S202, the third control unit 70I controls the modeling unit 72 so as to model a blood vessel model corresponding to the blood vessel model generated by the first generation unit 70D (step S202). Then, this routine ends.

  FIG. 10 is a flowchart illustrating a display control procedure executed by the processing device 70.

  First, the receiving unit 70F determines whether or not a blood vessel image display instruction has been received from the input unit 29 (step S300). For example, the first control unit 70E associates the generated blood vessel model with the identification information of the subject corresponding to the blood vessel model, and displays these lists on the display unit 31. The user operates the input unit 29 to select the display target of the blood vessel image from the list of blood vessel models and identification information displayed on the display unit 31. Then, the accepting unit 70F accepts a display instruction including identification information selected by the user from the input unit 29.

  If a negative determination is made in step S300 (step S300: No), this routine ends. On the other hand, if a positive determination is made in step S300 (step S300: Yes), the process proceeds to step S302.

  In step S302, the first control unit 70E reads the blood vessel model and the shape model corresponding to the identification information included in the display instruction received in step S300 from the storage unit 33, and displays the blood vessel image indicated by the shape model on the display unit. Control is performed to display on 31 (step S302).

  Next, the reception unit 70F determines whether an instruction for any position in the blood vessel image displayed on the display unit 31 has been received (step S304). If a negative determination is made in step S304 (step S304: No), this routine ends. If an affirmative determination is made in step S304 (step S304: Yes), the process proceeds to step S306.

  In step S306, the second generation unit 70G generates an output signal having an intensity corresponding to the material characteristic indicated by the additional information received in step S304 and corresponding to the instructed position in the blood vessel image (step S306).

  Next, the second control unit 70H controls the input unit 29 so as to generate a stress corresponding to the intensity of the output signal generated in step S306 (step S308). Then, this routine ends.

  As described above, the processing device 70 of the present embodiment includes the first acquisition unit 70A, the second acquisition unit 70B, the construction unit 70C, and the first generation unit 70D. 70 A of 1st acquisition parts acquire the image regarding the blood vessel of a subject. The second acquisition unit 70B acquires material characteristics at each position of the blood vessel. The constructing unit 70C constructs a shape model indicating the three-dimensional shape of the blood vessel from the medical image. The first generation unit 70D generates a blood vessel model in which additional information related to material characteristics is added to each position of the blood vessel indicated by the shape model.

  As described above, the processing apparatus 70 according to the present embodiment generates a blood vessel model corresponding to the material characteristics of the blood vessel of the subject.

  Therefore, the processing apparatus 70 according to the present embodiment can provide a blood vessel model corresponding to the material characteristics of the blood vessels of each subject.

  Specifically, it is particularly useful when performing surgical practice for inserting a catheter, stent, or the like into a blood vessel. That is, by using this blood vessel model for modeling a virtual simulator or a blood vessel model, the material properties (elastic modulus, flexibility, strength, hardness, and toughness) equivalent to the blood vessel of the subject to be actually verified are obtained. Surgery practice or the like can be performed using at least one blood vessel model or virtual simulator.

  In addition, the first generation unit 70D of the processing device 70 according to the present embodiment applies to the inner wall of the blood vessel wall so that the thickness of the blood vessel wall indicated by the blood vessel model corresponds to the material characteristics of each position of the blood vessel. A blood vessel model to which additional information obtained by correcting the relative position of the outer wall is added is generated.

  For this reason, in the blood vessel model, the inner diameter of the blood vessel is substantially the same as the inner diameter of the blood vessel indicated by the shape model constructed from the medical image of the subject, but the outer diameter of the blood vessel is determined by the material characteristics of the corresponding position. The thickness is corrected to a realizable thickness.

  For this reason, the processing apparatus 70 of this Embodiment can provide the blood vessel model according to the material characteristic of each subject in addition to the said effect.

  Moreover, the 3rd control part 70I can model the blood vessel model according to a blood vessel model by controlling the modeling part 72 so that the blood vessel model according to a blood vessel model may be modeled.

  For this reason, the modeling unit 72 is substantially the same as the inner diameter of the blood vessel indicated by the shape model constructed from the medical image of the subject with respect to the inner diameter of the blood vessel. It is possible to model a blood vessel model that has been corrected to have a thickness that can achieve the above.

  That is, the blood vessel model formed in the present embodiment has material characteristics (at least one of elastic modulus, flexibility, strength, hardness, and toughness) equivalent to the material characteristics of the blood vessels of the subject. It will be.

  For this reason, in addition to the above effects, the processing device 70 according to the present embodiment provides a blood vessel model that can perform surgical practice and the like in an environment close to a state in which the blood vessels of the subject to be actually verified are used. can do.

  Further, by using the blood vessel model formed by the modeling unit 72, it is possible to perform a surgical practice such as an operation in which a medical instrument such as a catheter is inserted into an existing site such as a plaque of a blood vessel of a subject. In addition, since the blood vessel model formed in the present embodiment has the same material characteristics as the blood vessel of the subject, how much blood vessel is used for a medical instrument such as a stent to be used for a lesion site in the blood vessel. It is possible to examine for each blood vessel of the subject such as whether it is effective for expansion and to what position it is effective to perform blood vessel expansion. For this reason, it is possible to estimate the therapeutic effect before the operation.

  In addition, the first generation unit 70D selects the type of modeling material at each position of the blood vessel model so that the modeling material at each position of the blood vessel model corresponding to the blood vessel model is a material that satisfies the material characteristics of each corresponding position. A blood vessel model to which additional information shown is added is generated.

  For this reason, the processing apparatus 70 of this Embodiment can provide the blood vessel model according to the material characteristic of each subject in addition to the said effect.

  In addition, the third control unit 70I controls the modeling unit 72 that models the three-dimensional structure so as to model the blood vessel model corresponding to the blood vessel model, thereby modeling the blood vessel model corresponding to the blood vessel model. it can.

  For this reason, the modeling unit 72 is substantially the same as the shape and size indicated by the shape model for the inner diameter, outer diameter, and shape of the blood vessel, but the type of modeling material that can realize the material characteristics of the blood vessel of the subject. A blood vessel model can be modeled using for each position.

  In addition, the first generation unit 70D provides a reinforcing member that reinforces the blood vessel indicated by the blood vessel model from the outside so that the strength of each position of the blood vessel model corresponding to the blood vessel model satisfies the material characteristics of each corresponding position. A blood vessel model to which additional information indicating the position is added is generated.

  For this reason, the blood vessel model has mechanical properties that satisfy actual material properties when the blood vessel is operated from the inside of the blood vessel.

  For this reason, in addition to the above effects, the processing device 70 according to the present embodiment provides a blood vessel model that can perform surgical practice and the like in an environment close to a state in which the blood vessels of the subject to be actually verified are used. can do.

  The first control unit 70E displays a blood vessel image indicated by the shape model on the display unit 31. Then, the second generation unit 70G generates an output signal having an intensity corresponding to the material characteristic indicated by the additional information corresponding to the position designated by the user in the displayed blood vessel image. The second control unit 70H controls the input unit 29 operated by the user so as to generate a stress corresponding to the intensity of the output signal.

  For this reason, the user feels a stronger stress as the user designates a position having a higher material property in the blood vessel image.

  Therefore, the material characteristics of the actual blood vessel of the subject can be tactilely provided to the user via the blood vessel image displayed on the display unit 31.

  Next, the hardware configuration of the processing device 70 according to the embodiment and the blood vessel analysis device 50 described later in detail will be described. FIG. 11 is a block diagram illustrating a hardware configuration example of the processing device 70 and the blood vessel analysis device 50.

  The processing device 70 and the blood vessel analysis device 50 include a CPU 800, a ROM (Read Only Memory) 820, a RAM (Random Access Memory) 840, an HDD (Hard Disk Drive) (not shown), and a communication I / F (Interface) 860. . The CPU 800, the ROM 820, the RAM 840, the HDD (not shown), and the communication I / F 860 are connected to each other via a bus and have a hardware configuration using a normal computer.

  A program for executing various processes executed by the processing device 70 and the blood vessel analysis device 50 according to the embodiment is provided by being incorporated in advance in the ROM 820 or the like.

  The programs for executing the various processes executed by the processing device 70 and the blood vessel analysis device 50 according to the embodiment are files that can be installed in these devices or in executable files, such as CD-ROMs, floppy disks. (Registered trademark) A disc (FD), a CD-R, a DVD (Digital Versatile Disk), or the like may be recorded and provided on a computer-readable recording medium.

  In addition, by storing a program for executing the various processes executed by the processing device 70 and the blood vessel analysis device 50 according to the embodiment on a computer connected to a network such as the Internet, the program is downloaded via the network. It may be configured to provide. In addition, the program for executing the various processes executed by the processing device 70 and the blood vessel analysis device 50 according to the embodiment may be provided or distributed via a network such as the Internet.

  A program for executing the above-described various processes executed by the processing device 70 and the blood vessel analysis device 50 according to the embodiment has a module configuration including the above-described functional units. As actual hardware, the CPU 800 reads each program from a storage medium such as the ROM 820 and executes the program, so that each functional unit is loaded onto the main storage device and generated on the main storage device.

(Vessel analysis device)
Next, the blood vessel analysis device 50 will be described in detail.

  Note that the medical image diagnostic apparatus according to the embodiment can analyze blood vessels in any part of the human body such as a cardiovascular vessel, a carotid artery, or a cerebral artery. Hereinafter, as an example, description will be made assuming that the blood vessel of the heart is the analysis target.

  Examples of the blood vessel of the heart include a coronary artery and an aorta. The coronary artery starts from the coronary artery origin of the aorta, travels on the myocardial surface, and enters the intima side from the epicardium side. The coronary arteries branch into myriad capillaries in the intima of the myocardium. After bifurcation, countless capillaries reintegrate to form the great cardiac vein and connect to the coronary sinus. Unlike other organs, the coronary vasculature is unique in that perfusion must be ensured in the course of mechanical changes such as myocardial contraction and relaxation.

  A characteristic of coronary blood flow is that it flows more when the perfusion pressure decreases in the left ventricular diastole than in the systole where the internal pressure at the coronary artery origin increases due to the mechanical blood flow inhibition effect due to myocardial contraction. Therefore, the normal coronary blood flow velocity waveform is bimodal in the systolic and diastolic phases, and the diastolic blood flow is dominant. In hypertrophic cardiomyopathy and aortic stenosis, retrograde waves are observed in the systole, and in aortic reflux, the systolic antegrade wave is increased, resulting in a specific blood flow waveform depending on the disease. Yes. In addition, the antegrade waveform in the diastole is closely related to the left ventricular dilation function, particularly the left ventricular relaxation. In the left ventricular relaxation delay example, the peak of the diastolic waveform shifts backward, and the deceleration leg tends to be gentle. In such cases, coronary blood flow during diastole cannot be sufficiently increased during tachycardia, which is thought to promote myocardial ischemia.

  Coronary blood flow is generated by applying coronary perfusion pressure equal to the aortic pressure (that is, the pressure at the aortic root where the coronary artery branches) to the left and right coronary arteries anatomically branching from the aortic root. Coronary blood flow resistance as well as driving pressure, which is aortic pressure, is important for determining coronary blood flow. A thick coronary vessel of 140 to 180 μm or more has about 20% of the resistance to coronary vessels, whereas a microvessel of 100 to 150 μm or less is said to have a lot of remaining resistance components. Therefore, in the absence of so-called coronary stenosis, the resistance value depends on the tonicity of coronary microvessels.

  Vascular resistance factors include vascular properties, arteriosclerosis, vascular stenosis, blood viscosity, and mechanical factors. Coronary microvessel tonus is defined by vascular properties, myocardial metabolism (myocardial oxygen consumption), neurohumoral factors, mechanical factors, various vasoactive substances as humoral factors, blood viscosity, cardiac hypertrophy, coronary artery Coronary circulatory disturbance is also affected by various lesions including sclerosis.

  Coronary blood flow pulsation is affected by the pulsation pattern of coronary blood flow, the control of intramyocardial blood flow by myocardial contraction, and the intramyocardial vascular response to mechanical stimulation. The mechanism by which myocardial contraction inhibits blood flow includes an increase in intramyocardial pressure, changes in intramyocardial vascular volume, and compression of intramyocardial blood vessels. The blood flow regulating factor in the myocardial diastole includes diastole coronary artery pressure, diastole extravascular force, heart rate, ratio of diastole to cardiac cycle, and myocardial relaxation.

  The blood vessel analysis device 50 constructs a dynamic model based on the time-series CT images, performs structural fluid analysis on the blood vessels of the heart using the dynamic model, and calculates the mechanical index and the blood flow rate index in the blood vessel. Calculate with high accuracy. In order to calculate a highly accurate mechanical index or vascular flow index, it is necessary to assign a highly accurate latent variable to the dynamic model. When constructing a dynamic model, the blood vessel analyzing apparatus 50 performs inverse analysis on the initial dynamic model and statistically identifies latent variables. Thereby, the blood vessel analysis device 50 can determine a highly accurate latent variable.

  The mechanical index means a mechanical index related to a blood vessel wall or blood. Examples of the mechanical index related to the blood vessel wall include an index related to the displacement of the blood vessel wall, an index related to stress and strain generated in the blood vessel wall, an index related to the distribution of internal pressure applied to the blood vessel lumen, and an index related to material properties representing the hardness of the blood vessel. And so on. Examples of the index relating to the material property representing the hardness of the blood vessel include an average slope of a curve indicating the relationship between stress and strain of the blood vessel tissue.

  The blood vessel flow rate index in the mechanical index related to blood means a hemodynamic index related to blood flowing through the blood vessel. Examples of the blood vessel flow rate index include blood flow rate, blood flow rate, blood viscosity, and the like. The latent variable is, for example, a material model parameter such as a blood vessel material constitutive equation or a blood material constitutive equation (eg, Young's modulus or Poisson's ratio), a load condition parameter such as an internal pressure distribution applied to the vascular lumen, It includes at least one of boundary condition parameters for structural analysis and fluid analysis, and variation distribution parameters related to uncertainties of time-series form indices and shape deformation indices.

  The material model parameters and material parameters correspond to the above-described material characteristics.

  Here, the variation distribution parameter related to the uncertainty of the time-series shape index and the shape deformation index means that the medical image data includes a variation distribution caused by noise of each CT value and an ambiguity of a boundary threshold value of a living tissue. Uncertainty in spatial coordinates of boundary coordinates and feature points (blood vessel bifurcations, contrast agent dispersion, etc.) of vascular tissue and blood, or geometric structure The uncertainty of a parameter (such as a lumen radius of a cross section perpendicular to the core line) or the uncertainty of medical image data itself (such as a CT value or a boundary threshold value) is expressed as a probability distribution.

  The dynamic model is a numerical model for expressing the behavior of blood vessels and blood. The mechanical model has different types depending on the method of structural fluid analysis. For example, the dynamic model is classified into a continuum dynamic model and a simple dynamic model. The continuum mechanical model is used, for example, in a finite element method (FEM) or a boundary element method. The simple dynamic model is classified into, for example, a material dynamic model based on material dynamics and a hydrodynamic model based on rheology.

  Note that the type of the dynamic model is not particularly limited unless otherwise specified in the following description. The initial dynamic model means a dynamic model to which a sampling set (a set of combinations of parameters) related to the parameters of the latent variable obtained from the probability distribution of the latent variable and the variable range is assigned.

  FIG. 12 is a diagram illustrating an example of a dynamic model M1 related to a target region for structural fluid analysis (hereinafter referred to as an analysis target region). As shown in FIG. 12, the dynamic model M1 has an aorta region R1, a right coronary artery region R2, and a left coronary artery region R3. Blood flows from the aorta to the right or left coronary artery.

  As shown in FIG. 12, the end of the dynamic model M1 on the side of the aortic root is set as the blood flow inlet, and the right coronary artery end and the left coronary artery end are set as the blood flow outlet. A boundary condition is set for each of the inlet and the outlet. The boundary conditions regarding the inlet include, for example, the blood flow velocity at the inlet or the pressure due to the blood flow, or the rate of change thereof. Boundary conditions for the outlet include, for example, the blood flow velocity at the outlet or the pressure due to blood flow, or the rate of change thereof.

  Deformation of the aorta, right coronary artery, and left coronary artery is due to mechanical action on the blood vessel wall due to blood flow, mechanical action on the blood vessel wall due to heartbeat (external force), load condition at the blood vessel cross-sectional boundary, blood vessel wall Depending on various factors such as the material model, the unstressed state of the vessel, and the geometry of the vessel wall.

  Here, the mechanical action on the blood vessel wall caused by the blood flow includes, for example, an internal pressure caused by the blood flow and a shearing force caused by the blood flow. Due to the internal pressure caused by the blood flow, the blood vessel is deformed in the radial direction of the blood vessel or in the direction perpendicular to the surface of the blood vessel lumen. The mechanical action on the blood vessel wall due to the pulsation of the heart and the shearing force due to the blood flow cause deformation due to the mechanical action on the blood vessel wall such as expansion / contraction, twisting and bending in the blood vessel core direction.

  The overall and local deformation behavior of the blood vessel, such as stretching, twisting, and bending with respect to the blood vessel core direction, is forced displacement in the aorta region R1, the right coronary artery region R2, and the left coronary artery region R3, or a part thereof, or the surrounding tissue. A load condition is set by giving a temporal change in (movement vector or rotational displacement) or load vector. Further, the deformation in the radial direction of the blood vessel based on the internal pressure caused by the blood flow or the direction perpendicular to the lumen surface is given as a temporal change in the pressure distribution to the blood vessel lumen, thereby setting the load condition.

  In the structural fluid analysis, a displacement constraint condition by forced displacement is assigned to the aorta region R1, the right coronary artery region R2, and the left coronary artery region R3. As a result, the degree of freedom of deformation of the blood vessel wall in the structural fluid analysis can be reduced, and the calculation convergence can be stabilized and the analysis time can be shortened.

  Further, for example, the degree of deformation of the blood vessel shape depends on the material of the blood vessel wall. Therefore, material models are assigned to the aorta region R1, the right coronary artery region R2, and the left coronary artery region R3. Also, for example, the degree of deformation of the blood vessel shape depends on the unstressed state of the blood vessel. As an initial value of the load condition, a residual stress distribution of the blood vessel may be given as the load condition.

  Here, in the element set composed of the node set and nodes that discretize the space of the numerical model for blood vessel analysis, identify the region that does not identify the analysis conditions such as the material model, boundary condition, load condition, etc. Displacement constraint of forced displacement history is given to the nodes in the region where analysis conditions are not identified, and the displacement constraint condition of forced displacement history is given only to the nodes on the blood vessel wall surface (outer surface) in the region where the material model is identified. The inside of the blood vessel wall secures a degree of freedom of displacement and does not give displacement restraint. Thereby, the deformation | transformation freedom degree of structural fluid analysis can be suppressed, and it can analyze stably and efficiently.

  However, a dummy dummy set for buffering may be provided on the surface of the blood vessel wall, and a forced displacement may be applied to a node on the surface. As a result, when there is a projection due to plaque or the like in the blood vessel lumen, or when a load due to internal pressure such as a blood vessel bifurcation affects the deformation outside the cross section in the core line direction, not only the blood vessel lumen but also the blood vessel wall The load vector applied to the blood vessel and the internal pressure can be separated and identified with reference to the shape index. Physiologically, it simulates the fat layer on the wall surface. On the other hand, in the numerical calculation, forced displacement is applied to the surface of the blood vessel wall, and high stresses that are different from the actual locality are generated inside the blood vessel wall. There is also an effect to avoid that.

  Parameters related to latent variables such as the material model, boundary condition, and load condition are identified by inverse analysis (statistical identification processing) based on a dynamic model described later. Accurate latent variables identified by inverse analysis are assigned to the dynamic model. For structural fluid analysis or fluid analysis or structural analysis or image analysis that takes into account the influence of external factors such as blood vessels and heart outside the analysis target blood vessel region to the analysis target blood vessel region using a dynamic model to which precise latent variables are assigned Based on hemodynamic analysis can be performed.

  The blood vessel analysis device 50 can solve the following four difficulties by identifying latent variables by inverse analysis regarding the construction of a dynamic model. Difficult Identification method of coronary artery material model. Difficult 2 Incorporation of coronary effects of heart shape deformation. Difficult 3 A method for identifying boundary conditions of coronary arteries. Difficult 4 Image analysis and structural fluid analysis using blood vessel shapes with variations due to the uncertainty of medical image data. By overcoming these four difficulties, the blood vessel analysis device 50 achieves improved analysis accuracy compared to conventional blood vessel structure fluid analysis in which latent variables are not identified by inverse analysis.

  Next, details of the structural fluid analysis process in the medical image diagnostic apparatus of the embodiment will be described. FIG. 13 is a diagram showing a typical flow of structural fluid analysis processing performed under the control of the system control unit 21. FIG. 14 is a functional block diagram of the analysis device 27.

  As shown in FIG. 13, in the structural fluid analysis process, first, the medical image file to be processed is read from the storage unit 33 by the system control unit 21 and supplied to the analysis device 27. The medical image file includes, in addition to time-series CT images, pressure value data related to the subject's blood vessel lumen, blood flow index observed value data, and plaque index. Other than the CT image, an MRI image or an ultrasonic echo image may be used. A time-series CT image is data representing a three-dimensional spatial distribution of time-series CT values. The time-series CT images include, for example, about 20 CT images for one heartbeat, that is, about 20 heart phases.

  As shown in FIG. 13, the system control unit 21 causes the analysis device 27 to perform an area setting process (step S1). In step S1, the region setting unit 51 of the analysis device 27 sets an analysis target region for structural fluid analysis in a blood vessel region included in a time-series CT image. The analysis target region is set to an arbitrary part of the blood vessel region related to the coronary artery. For example, the region setting unit 51 sets the analysis target region and the identification target region in the blood vessel region by an instruction from the user via the input unit 29 or image processing.

  Here, the structure of the blood vessel will be described with reference to FIG. FIG. 15 is a diagram schematically showing a cross section (hereinafter, referred to as a blood vessel cross section) substantially orthogonal to the core line of the blood vessel. As shown in FIG. 15, the blood vessel has a tubular blood vessel wall. The central axis of the blood vessel wall is called the core wire. The inside of the vessel wall is called the lumen. Blood flows into the lumen. The boundary between the lumen and the blood vessel wall is called the blood vessel inner wall. A tissue around a blood vessel such as a myocardium is distributed outside the blood vessel wall. The boundary between the blood vessel wall and the tissue surrounding the blood vessel is called the blood vessel outer wall. Plaques may develop inside the vessel wall.

  As shown in FIG. 15, plaques are classified into, for example, calcified calcified plaques, rod-shaped plaques, and the like. Spider-like plaque is soft and has a risk of rupturing the inner wall of the blood vessel and oozing out into the blood vessel as a thrombus, and is sometimes called unstable plaque. Therefore, it is clinically useful to grasp the characteristics of plaque. Plaque properties and existing areas can be identified by a plaque index included in the medical image file. The plaque index can be relatively discriminated by, for example, the magnitude of the CT value normalized with reference to the CT value of bone. However, it is not easy to analyze the deformation characteristics and hardness of the plaque inside the blood vessel.

  When step S1 is performed, the system control unit 21 causes the analysis device 27 to perform image analysis / tracking processing (step S2). In step S2, the image analysis / tracking processing unit 53 of the analysis device 27 performs image processing on the time-series CT image to calculate a time-series blood vessel shape index and a time-series blood vessel shape deformation index. Specifically, the image analysis / tracking processing unit 53 calculates a time-series blood vessel shape index by performing an image analysis process on the time-series CT image, and performs a tracking process on the time-series CT image. A series of blood vessel shape deformation indices is calculated.

  More specifically, the image analysis / tracking processing unit 53 extracts a blood vessel region from each CT image in the image analysis processing, and relates to a pixel region relating to a blood vessel lumen (hereinafter referred to as a blood vessel lumen region) and a blood vessel wall. A pixel region (hereinafter referred to as a blood vessel wall region) is specified. The image analysis / tracking processing unit 53 uses, as a blood vessel shape index, a plurality of pixels on a region where a cross section perpendicular to a blood vessel core line or a surface perpendicular to a blood vessel lumen surface intersects a blood vessel lumen, a blood vessel wall, and a plaque region. The three-dimensional coordinates of are specified.

  The blood vessel shape index is not only a three-dimensional coordinate, but also a radius and diameter of a blood vessel lumen at a certain angle in a cross section perpendicular to the core line and a direction vector of 0 °, or an average area and average radius for all angles in the cross section, Alternatively, a blood vessel lumen volume surrounded by a plurality of cross sections perpendicular to the core line direction, or a geometric index such as a blood vessel wall volume or a plaque volume surrounded by a plurality of cross sections perpendicular to the lumen surface may be used.

  In the tracking process, the image analysis / tracking processing unit 53 displays a feature point, a feature shape, a representative point, a pixel, or the like in a blood vessel region, blood, a contrast medium, or a proton by an indication or image processing via the input unit 29 from the user. Set multiple tracking points. For example, the image analysis / tracking processing unit 53 sets a tracking point set such as a blood vessel bifurcation or a surface feature shape. From the displacement data of the tracking point set obtained by the tracking processing of the image analysis / tracking processing unit 53 at each time (each cardiac phase), the displacement time of the node on the blood vessel wall surface, the blood vessel wall, or the blood vessel lumen of the dynamic model The change is calculated by interpolation processing or the like and given as a forced displacement.

  For example, the image analysis / tracking processing unit 53 defines a node on the blood vessel core line in the dynamic model. The image analysis / tracking processing unit 53 extracts deformation related to expansion / contraction, torsion, and bending in the core direction of the blood vessel from temporal changes in the displacement of the nodes on the surface of the blood vessel wall in the mechanical model, in the blood vessel wall, or in the blood vessel lumen. You may express by giving as a forced displacement of the node in the cross section perpendicular | vertical to a core wire and a core wire. Thus, the forced displacement data (forced displacement history) at the nodes at each time in the dynamic model is specified as the blood vessel shape deformation index.

  Hereinafter, the image analysis / tracking process will be described with reference to FIGS. 16 and 17. FIG. 16 is a diagram showing temporal changes in the shape of the blood vessel core line. As shown in FIG. 16, for example, a time-series medical image is assumed to include 20 CT images per heartbeat. That is, CT images are obtained every 5% from 0% to 95% of the cardiac phase. The image analysis / tracking processing unit 53 extracts the core line of the blood vessel region. As shown in FIG. 16, the form of the core wire changes as the cardiac phase progresses.

  FIG. 17 is a diagram illustrating an example of the tracking process between time t and time t + Δt. As shown in FIG. 17, nodes in the dynamic model from P1 to P10 are set on the blood vessel core line, and are dynamically connected to the nodes of the blood vessel dynamic model on each cross section. However, it is independent of the nodes of the blood dynamic model. It is assumed that the displacement data of the nodes P1 to P20 on the blood vessel core line is calculated based on the displacement data of the blood vessel tracking points by a process such as interpolation, and the forced displacement is set at each node. In order to describe the blood vessel shape deformation index and the blood vessel shape index, a local blood vessel region RA defined by the node P13 and the node P14 is considered.

  It is assumed that the distance between the node P13 and the node P14 in the core line direction is L and the radius of the blood vessel region is r at time t. From the image analysis / tracking processing unit 53, a forced displacement such as expansion, contraction, torsion, or bending in the blood vessel core direction between the node P13 and the node P14 is extracted, and the forced displacement of the node P13 (the movement displacement in the three-dimensional space and the core line direction). Rotational displacement) and forced displacement of the node P14 (moving displacement in the three-dimensional space and rotational displacement in the core line direction) are calculated.

  FIG. 18 is a diagram illustrating a calculation example of bending deformation and rotational displacement of the blood vessel core wire. As shown in FIG. 18, for example, the torsion angle may be calculated from a change in the normal direction vector c of the surface composed of the lines a and b.

  As shown in FIGS. 17 and 18, the image analysis / tracking processing unit 53 performs forced displacement of each node on the core line (movement displacement in the three-dimensional space and the core line direction based on the coordinates of the tracking point and the movement vector. ), And a blood vessel shape deformation index is calculated. For example, the image analysis / tracking processing unit 53 calculates the temporal change in the coordinate difference between two adjacent nodes as the expansion / contraction distance ΔL in the core line direction.

  Further, the image analysis / tracking processing unit 53, for each node on each core line, between the node and another node on the blood vessel region cross section including the node (node in the blood vessel lumen, blood vessel wall, or plaque region). The time change of the distance is calculated as the expansion / contraction distance Δr in the radial direction. Further, the image analysis / tracking processing unit 53 calculates, for each tracking point, a twist angle Δθ in the core line direction of the node on the core line based on the coordinates and movement vectors of a plurality of tracking points in the vicinity of the tracking point. To do.

  Further, the image analysis / tracking processing unit 53 may calculate a flow velocity or an average flow velocity or average flow rate in the cross section in the core line direction as a blood flow index by image tracking of a contrast agent or proton in the blood region.

  The blood vessel shape deformation index is used as a forced displacement in the dynamic model. Hereinafter, the time-series blood vessel shape index is referred to as “shape history”, and the time-series blood vessel shape deformation index is referred to as “forced displacement history”.

  When step S2 is performed, the system control unit 21 causes the analysis device 27 to perform a construction process (step S3). In step S <b> 3, the dynamic model construction unit 55 of the analysis device 27 performs shape history (time-series blood vessel shape index), forced displacement history (time-series blood vessel shape deformation index), and time-series medical image (CT image, MRI). Based on the DICOM data (image, ultrasonic echo image, etc.), a dynamic model relating to the analysis target region is provisionally constructed. The dynamic model is a numerical model related to an analysis target region for performing structural fluid analysis.

  Hereinafter, step S3 will be described in detail. The dynamic model construction unit 55 first constructs a shape model for solving a dynamic model (mathematical model) based on the medical image and the shape history. The shape model schematically represents the geometric structure of the blood vessel region at each time. The shape model is divided into a plurality of discretized regions, for example. The vertices of each discretized region are called nodes.

  The dynamic model construction unit 55 may construct a shape model for each time based on a blood vessel region and a blood vessel shape index included in the medical image for each time, or a blood vessel included in the medical image for a specific time phase. A shape model for each time may be constructed based on the region and the blood vessel morphology index. For example, when it is assumed that there is no residual stress in the blood vessel corresponding to the analysis target region as the initial load state, the time phase in which the blood vessel corresponding to the analysis target region contracts most Assume no stress.

  FIG. 19 is a diagram showing a cross section orthogonal to the core wire of the shape model. As shown in FIG. 19, the shape model has a blood vessel lumen region and a blood vessel wall region from the core line to the outside. When plaque exists, a plaque region may be provided in the blood vessel wall region. Further, when considering the influence on the blood vessel by the blood vessel peripheral tissue, a dummy element of the blood vessel peripheral tissue may be provided outside the blood vessel wall region.

  When the shape model is constructed, the dynamic model construction unit 55 obtains a sampling value relating to the parameter of the latent variable obtained from the probability distribution and the variable range of each latent variable (for example, from a set of combinations of each parameter by Markov chain Monte Carlo method or the like). Set Sampling to the dynamic model.

  For example, as shown in FIG. 12, the dynamic model construction unit 55 sets an identification target region (hereinafter referred to as a boundary condition identification region) for the boundary condition related to the entrance at the end of the aortic region R1 on the aortic root start side. A boundary condition identification region relating to the exit is set at the end of the coronary artery region R2 and the end of the left coronary artery region R3. The dynamic model construction unit 55 assigns a sampling value related to a boundary condition parameter obtained from the probability distribution of the boundary condition or the variable range to each boundary condition identification region.

  The dynamic model construction unit 55 also includes a material model identification target region (hereinafter referred to as a material model identification region) and a load condition identification target region (hereinafter referred to as a material model identification region) in the aorta region R1, the right coronary artery region R2, and the left coronary artery region R3. Hereinafter, this is referred to as a load condition identification region). The mechanical model construction unit 55 assigns a sampling value related to a material model parameter obtained from the probability distribution or variable range of the material model to each material model identification region, and obtains from each of the load condition identification regions from the probability distribution of the load condition and the variable range. Assign a sampling value for the load condition parameter

  It is said that blood vessels have residual stress even when the flow rate is zero. For example, the dynamic model construction unit 55 may assign the residual stress when the flow rate is 0 to the analysis target region as the initial value of the load condition. In addition, the dynamic model construction unit 55 may set a region for which the geometric structure is to be identified (hereinafter referred to as a geometric structure identification region) in a region where the geometric structure is uncertain. The geometric structure parameter is a variation distribution parameter related to the uncertainty of the geometric structure or a variation distribution parameter inherent in the medical image data. The variation distribution of each CT value or the boundary threshold value of the living tissue Or the like. Although details will be described later, the dynamic model construction unit 55 may set a material model in the plaque region. Details of the material model will be described later.

  When the shape model is constructed, the dynamic model construction unit 55 assigns the time-series blood vessel shape deformation index calculated in step S2, that is, the forced displacement history, to the shape model. A shape model to which a latent variable and a forced displacement history are assigned is called a “mechanical model”.

  FIG. 20 shows a part of a mechanical model of blood vessels and blood as a shape model M2, and is a diagram for explaining assignment of forced displacement history to nodes of the dynamic model. FIG. 20 shows a part of the shape model M2. However, although FIG. 20 shows the case where the core wire is located within M2, the core wire may be located outside M2. As shown in FIG. 20, a plurality of nodes PN (PN1, PN2) are set in the shape model M2. A node on the core line is referred to as PN1, and a node in a blood vessel or blood dynamic model is referred to as PN2. The shape model M2 is set on the dummy element surface, blood vessel outer wall, blood vessel inner wall, plaque region surface, plaque region inside, or blood part. The dynamic model construction unit 55 assigns a forced displacement, that is, a blood vessel shape change index to each node PN1 of the shape model M2 for each time.

  Specifically, the dynamic model construction unit 55 connects adjacent nodes PN1 and PN1 on the core line with beam elements (or rigid elements) EB. Further, the dynamic model construction unit 55 connects the node PN1 and the other node PN2 included in the orthogonal cross section passing through the node PN1 by the beam element EB. The dynamic model construction unit 55 assigns a constraint condition related to the shape displacement direction of each blood vessel shape deformation index to the node PN1 and the beam element EB.

  In the region that identifies the internal pressure of the material model or the blood vessel lumen, the forced displacement includes expansion / contraction of the surface of the blood vessel wall (or dummy element) with respect to the core line direction, twist of the surface of the blood vessel wall (or dummy element), blood vessel wall (or dummy element) ) Surface bending deformation. For example, in a region where the material model and the internal pressure of the blood vessel lumen are not identified, not only forced displacement in the core line direction but also time-series expansion / contraction (displacement) of the blood vessel wall in the radial direction is assigned as the forced displacement history.

  Also, when the internal pressure contributes to deformation outside the cross section in the core line direction, such as when there is a protrusion in the blood vessel lumen or in a blood vessel bifurcation, the area around that area is not forcedly displaced. A forced displacement history is assigned only to a portion (for example, a surface node of a dummy element). In addition, a time-series blood vessel shape deformation index is assigned to the dynamic model construction unit 55, the node PN1, and the beam element EB as a forced displacement history. As a result, expansion deformation, torsion deformation, and bending deformation related to the whole and local blood vessels are expressed.

  The assignment target of the forced displacement history is not limited to the nodes and beam elements on the core line. FIG. 21 and FIG. 22 are diagrams showing another method for assigning the forced displacement history to the shape model. FIG. 21 shows an example of assignment when the blood vessel shape deformation index is twisted. FIG. 22 shows an example of assignment when the blood vessel shape deformation index is bending. As shown in FIGS. 21 and 22, the dynamic model construction unit 55 may assign a forced displacement history directly to the surface or internal node of the shape model. For example, in step S <b> 2, the image analysis / tracking processing unit 53 calculates a blood vessel shape deformation index such as an expansion / contraction amount, a torsion amount, and a bending amount for the feature point. The dynamic model construction unit 55 directly assigns the calculated blood vessel shape deformation index by interpolating (interpolating or extrapolating) the nodes around the feature points.

  The analysis device 27 according to the embodiment performs inverse analysis using the mechanical model provisionally constructed in step S3, and statistically identifies latent variables set in the dynamic model. The statistical identification process is performed in step S6 described later. Steps S4 and S5 are provided for calculating a blood vessel shape index and a blood flow index used for the statistical identification process, respectively.

  When step 3 is performed, the system control unit 21 causes the processing device to perform vascular stress analysis processing on the processing device 27 (step S4). In step S <b> 4, the vascular stress analysis unit 57 of the analysis device 27 performs vascular stress analysis on the current dynamic model to calculate a predicted value of a time-series vascular shape index. The blood vessel shape index may be any of the above-described blood vessel shape fingers. For example, a cross-sectional shape index of a lumen region or a cross-sectional shape index of a blood vessel wall in the blood vessel core direction may be used.

  Specifically, the cross-sectional shape index of the lumen region is at least one of the coordinate value of the pixel of interest in the lumen region and the geometric structure parameters of the lumen region (the radius of the lumen region, the diameter of the lumen region, etc.). One. Further, the cross-sectional shape index of the blood vessel wall region specifically includes at least the coordinate value of the target pixel of the blood vessel wall region, the geometric structure parameters of the blood vessel wall region (the radius of the blood vessel wall region, the diameter of the wall region, etc.) One. Note that the predicted value means a calculated value of a blood vessel shape index calculated by performing vascular stress analysis on the dynamic model.

  When step 3 is performed, the system control unit 21 causes the analysis device 27 to perform blood fluid analysis processing (step S5). In step S <b> 5, the blood fluid analysis unit 59 of the analysis device 27 performs blood fluid analysis on the tentatively constructed dynamic model to calculate a predicted value of the time-series blood flow index. The blood flow index is at least one of a blood flow rate or a flow velocity, or a spatial and temporal average value thereof. Note that the predicted value means a calculated value of the blood fluid index calculated by performing blood fluid analysis on the dynamic model.

  When steps S4 and S5 are performed, the system control unit 21 causes the analysis device 27 to perform identification processing (step S6). In step S6, the statistical identification unit 61 of the analysis device 27 collects in advance the blood vessel shape index in which the predicted value of the blood vessel shape index calculated in step S4 and the predicted value of the blood flow rate index calculated in step S5 are collected. The parameters of the latent variables of the mechanical model are statistically identified so as to match the observed values of the blood flow and the observed values of the blood flow index.

  As illustrated in FIG. 14, the statistical identification unit 61 includes a first statistical identification unit 61-1 and a second statistical identification unit 61-2. The first statistical identification unit 61-1 statistically identifies the parameter of the latent variable so that the predicted value of the blood vessel shape index matches the observed value of the blood vessel shape index. The second statistical identification unit 61-2 statistically identifies the parameter of the latent variable so that the predicted value of the blood flow index matches the observed value of the blood flow index. Hereinafter, the first statistical identification unit 61-1 and the second statistical identification unit 61-2 will be described in order.

  Specifically, in step S6, the first statistical identification unit 61-1 sets a data distribution based on the predicted value and the observed value of the blood vessel shape index calculated in step S4. The data distribution indicates, for example, a multivariate normal distribution function related to an error between the predicted value and the observed value of the blood vessel shape index.

  A normal distribution function value regarding the error between the predicted value and the observed value at each node or each element in the dynamic model is calculated, and the product of these is set as a data distribution in the statistical identification processing unit. The data distribution may be set individually for each time or may be set collectively for a plurality of times.

  Next, the first statistical identification unit 61-1 assigns a prior distribution (prior probability distribution) to the latent variable of the dynamic model. Specifically, a prior distribution is assigned to each of the material model, the boundary condition, the load condition, and the parameters related to the uncertainty of the time-series shape index and shape deformation index. For example, a prior distribution relating to a pressure value related to a blood vessel lumen, which is one of parameters of a load condition, is assigned. The range of values that can be taken by the pressure value (assumed range) can be empirically limited in advance. The first statistical identification unit 61-1 calculates a probability distribution of internal pressure values, that is, a prior distribution, for each discretized region by executing a Monte Carlo simulation related to the internal pressure values within the assumed range.

  In addition, as a prior distribution, the dynamic model construction unit 55 has observed that the pressure distribution in the core line direction is smooth, that the pressure change with the passage of time is smooth, and that there is no backflow of blood flow. In this case, for example, a probability distribution expressed mathematically by a multivariate normal distribution function that the slope of the average pressure change in the core line direction is negative may be set as the prior distribution.

  It is possible to execute a Monte Carlo simulation regarding the parameters of the load condition according to the assumed probability distribution, and to obtain a sampling value of the load condition (latent variable) to be set in the dynamic model, within the assumed range. .

  Next, the first statistical identification unit 61-1 calculates a posterior distribution (a posteriori probability distribution) by performing a statistical identification process on the prior distribution and the data distribution for each latent variable. Examples of the statistical identification process include a hierarchical Bayes model and a Markov chain model. And the 1st statistical identification part 61-1 identifies the parameter of each latent variable from statistical values, such as the mode value and average value of posterior distribution, about each latent variable. For example, in the case of the above-described example, the posterior distribution related to the vascular lumen pressure value is calculated, and the identification value of the vascular lumen pressure value is calculated from the posterior distribution.

  FIG. 23 is a diagram for explaining the calculation of the posterior distribution and the identification of the average internal pressure related to the load condition (average pressure in the blood vessel) by the hierarchical Bayes model and the Markov chain Monte Carlo method. As shown in FIG. 23, it is assumed that a calcified plaque region and a hook-like plaque region are set in a blood vessel extending from the blood vessel starting portion. The calcified plaque region is set as the material model identification region, and the calcified plaque region is set as the material model identification region. The blood pressure in the blood vessel decreases as the blood vessel progresses along the blood vessel core direction from the blood vessel starting portion. A plurality of nodes are set along the blood vessel core line. The posterior distribution of the intraluminal pressure is calculated in the orthogonal section (node section) including each node, and the mode value of the posterior distribution is identified.

  For example, the blood vessel shape index calculated in step S2 is used as the observed value of the blood vessel shape index.

  The process by the second statistical identification unit 61-2 is the same as the process by the first statistical identification unit 61-1, except that the index used for calculating the data distribution is different. That is, the second statistical identification unit 61-2 first sets a data distribution based on the predicted value and the observed value of the blood flow index calculated in step S5.

  Next, the second statistical identification unit 61-2 assigns a prior distribution to the latent variables of the dynamic model. For example, a prior distribution relating to a material model parameter relating to blood vessels, a material model parameter relating to blood, and a material model parameter relating to plaque is assigned. Examples of parameters of these material models include material model parameters such as elastic modulus, and parameters related to viscosity in blood constitutive equations. The assumed range and probability distribution of the parameters of the material model can be set empirically in advance.

  The second statistical identification unit 61-2 sets the probability distribution of the parameter of the material model for each discretized region, that is, sets a prior distribution, limits the range within these assumed ranges, and follows the assumed probability distribution of the material model. A Monte Carlo simulation on the parameters can be executed, and a sampling value of the material model parameter (latent variable) for setting to the dynamic model can be obtained.

  Next, the second statistical identification unit 61-2 calculates a posterior distribution by applying a statistical identification process to the prior distribution and the data distribution for each latent variable, and calculates each statistic value of the calculated posterior distribution. Identify the parameters of the latent variable. For example, in the case of the above-described example, the posterior distribution relating to the parameter of the material model is calculated, and the identification value of the parameter of the material model is calculated from this posterior distribution.

  FIG. 24 is a diagram for explaining a posteriori distribution calculation and identification of a material model parameter (equivalent elastic modulus of a blood vessel wall) regarding a material model parameter by a hierarchical Bayes model and a Markov chain Monte Carlo method. As shown in FIG. 24, it is assumed that the blood vessel model is the same as that in FIG. The posterior distribution of the material model parameter (for example, equivalent elastic modulus) of the blood vessel wall is calculated only in the material model identification region, and the mode value of the posterior distribution is identified.

  Note that the observed value of the blood flow index is assumed to be, for example, a change in the blood flow sent to the aorta, and the observed value of the blood vessel shape index is changed from the time-series CT image by image processing. It can be used as a value (CFA). The flow rate and flow rate may be calculated by calculating the temporal change in the amount of movement of the feature points by image tracking of the contrast agent after injection of the contrast agent into the coronary artery. In addition, the concentration change amount of a specific region in the blood vessel core direction or temporal direction of the contrast agent is acquired, and the flow rate and the value of the concentration change divided by the distance in the core direction direction of each region and the temporal change rate of the concentration change are calculated. The flow rate may be calculated. In the case of MRI, proton image tracking is used, and in the case of ultrasonic echoes, the flow rate is calculated by contrast echography or the like.

  In addition, if it is not assumed that the coordinate value of each pixel in the analysis target area is a definite value, that is, if it is assumed that the geometric structure of the analysis target area is uncertain, the geometrical A structure may be included. In this case, the statistical identification unit 61 pre-distributes a probability distribution such as a normal distribution expressing a variation within a predetermined range of the coordinate value of each node in the core line direction or a variation within a predetermined range of the diameter of the analysis target region. It is good to set as. In this case, a restriction that the shape of the analysis target area is smooth and that the order of the nodes in the core line is unchanged may be set as a prior distribution. It is possible to perform Monte Carlo simulations on geometric parameters according to the assumed probability distribution, and limit the uncertainty parameters of the geometric structure (latent variables) to be set to the dynamic model. ) Sampling value can be obtained.

  In each step S6, both the statistical identification process by the first statistical identification unit 61-1 and the statistical identification process by the second statistical identification unit 61-2 may not be performed. That is, in each step S6, either the statistical identification process by the first statistical identification unit 61-1 or the statistical identification process by the second statistical identification unit 61-2 may be performed.

  In the above example, the first statistical identification unit 61-1 statistically identifies the parameters of the latent variable so that the predicted value of the blood vessel shape index matches the observed value of the blood vessel shape index, and the second The statistical identification unit 61-2 statistically identifies the parameters of the latent variable so that the predicted value of the blood flow index matches the observed value of the blood flow index. However, based on the structure-fluid coupling analysis, the statistical identification unit 61 matches the predicted value of the blood vessel shape index and the predicted value of the blood flow index with the observed value of the blood vessel shape index and the observed value of the blood flow index. Thus, the parameters of the latent variable may be identified statistically. Further details of the statistical identification process by the statistical identification unit 61 will be described later.

  When step S6 is performed, the system control unit 21 causes the analysis device 27 to perform setting processing (step S7). In step S7, the dynamic model construction unit 55 of the analysis device 27 sets the parameter of the latent variable calculated in step S6 to the dynamic model.

  When step S7 is performed, the system control unit 21 determines whether or not the identification end condition is satisfied (step S8). If it is determined in step S8 that the identification end condition is not satisfied (step S8: NO), the system control unit 21 repeats steps S4, S5, S6, S7, and S8.

  Here, the identification end condition is expressed by whether or not an index for determining the end of identification (hereinafter referred to as an identification end index) reaches a specified value. As an identification end index, for example, a difference value between a predicted value and an observed value of a blood vessel morphology index can be cited. In this case, the system control unit 21 determines that the identification end condition is not satisfied when the difference value is larger than the predetermined value, and determines that the identification end condition is satisfied when the difference value is smaller than the predetermined value. To do.

  Further, the identification end index may be, for example, the number of sampling points of the Monte Carlo method. In this case, when the number of sampling points is smaller than the predetermined value, the system control unit 21 determines that the identification end condition is not satisfied, and when the number of sampling points is larger than the predetermined value, the identification end condition is Determined to be satisfied. When the identification end condition is satisfied, the dynamic model construction unit 55 sets the latest dynamic model at that time as the final dynamic model.

  When the final mechanical model is constructed, the mechanical model construction unit 55 associates the observed value of the blood vessel shape deformation index, the load condition parameter in the final mechanical model, and the material model parameter (hereinafter referred to as the related model). Called a model). The related model is stored in the storage unit 33. The related model may be stored in association with patient information, examination information, or the like for ease of search. Note that the observed values of the blood vessel shape index and blood flow index, the load condition parameter in the final dynamic model, and the material model parameter do not necessarily have to be associated with each other in the form of the model. Also good.

  The above steps S4, S5, S6, S7, and S8 may be repeated with the same identification method, or may be repeated with different identification methods. When iterating with different identification methods, for example, first, a latent variable may be tentatively identified using a simple dynamic model, and then a latent variable may be accurately identified using a continuum dynamic model. . Thus, by performing the statistical identification process in two steps using different methods, the parameters of the latent variable can be converged in a short time. As a method of using a simple dynamic model, there is a method of using internal pressure and external pressure for the material dynamics equation of the thick-walled cylinder, Hagen-Poiseuille flow and modified Bernoulli equation. As a method using the continuum mechanics model, FEM structural fluid analysis can be mentioned. Details of the identification method using a simple mechanical model and the identification method using a continuum mechanical model will be described later.

  If it is determined in step S8 that the identification completion receipt is satisfied (step S8: YES), the system control unit 21 may cause the image analysis / tracking processing unit 53 to perform correction processing (step S9). In step S9, the image analysis / tracking processing unit 53 performs the structural fluid analysis result (predicted value of the mechanical index and predicted value of the blood fluid index) performed based on the latent variable obtained by the inverse analysis by the statistical identification method. The shape of the blood vessel region included in the time-series medical image may be corrected so as to match the observed value (the observed value of the mechanical index and the observed value of the blood fluid index).

  The display unit 31 displays the diagnosis result based on the corrected time-series medical image. Thereby, the blood vessel analysis device 50 can display the diagnosis result in consideration of the final dynamic model. Alternatively, the display unit 31 may display on the screen a blood vessel location / region where the identification by the inverse analysis and the observation result by the structural fluid analysis do not match.

  For example, an image of a cardiac phase in which the behavior of a blood vessel is fast is often blurred, and there are locations and regions with large errors in the blood vessel shape observed by image analysis based on a medical image. The cardiac phase data in which the blood vessel behavior is relatively stable can be obtained from an image with little noise. Based on such blood vessel shape data with a small error distribution, the blood vessel shape in the cardiac phase with a large error can be correctly interpolated by using a dynamic model. Along with the correctly interpolated shape, it can be displayed with the variation distribution from the original data. Thereby, the stability of the blood vessel shape display can be ensured and the user can recognize the uncertainty of the shape.

  When step S9 is performed, the system control unit 21 causes the analysis device 27 to perform vascular stress analysis processing (step S10). In step S <b> 10, the vascular stress analysis unit 57 of the analysis device 27 performs vascular stress analysis on the final dynamic model to calculate a spatial distribution of predicted values of time-series mechanical indices. Specifically, the predicted value of the mechanical index is calculated for each discretized region.

  When step S9 is performed, the system control unit 21 causes the analysis device 27 to perform blood fluid analysis processing (step S11). In step S11, the blood fluid analysis unit 59 of the analysis device 27 performs blood fluid analysis on the tentatively constructed dynamic model to calculate a spatial distribution of predicted values of the time-series blood flow index. Specifically, the predicted value of the blood flow index is calculated for each discretized region.

  Note that FFR may be calculated as a mechanical index or a blood flow index.

  When steps S9 and S10 are performed, the system control unit 21 causes the display unit 31 to perform display processing (step S11). In step S11, the display unit 31 displays the predicted value of the time-series dynamic index calculated in step S9 and the predicted value of the time-series blood flow index calculated in step S10.

  For example, the display unit 31 displays a time-series dynamic index or a time-series blood flow index in a moving image of a time-series dynamic model in a color corresponding to the predicted value. Therefore, the display unit 31 holds a color table indicating the relationship between various predicted values and color values (for example, RGB). The display unit 31 specifies a color value corresponding to the predicted value using a color table, and displays a discretization region corresponding to the predicted value with a color corresponding to the specified color value.

  FIG. 25 is a diagram illustrating a display example of a spatial distribution of internal pressure that is one of the mechanical indices. As shown in FIG. 25, the display unit 31 displays each discretized area constituting the dynamic model in a moving image with a color corresponding to the internal pressure value related to the discretized area. By observing the dynamic model, the user can grasp the dynamic index that changes with time and space by color.

  FIG. 26 is a diagram illustrating a display example of a spatial distribution of flow velocity values that are one of blood flow indices. As shown in FIG. 26, the display unit 31 displays each discretized area constituting the dynamic model in a moving image with a color corresponding to the flow velocity value related to the discretized area. By observing the dynamic model, the user can grasp the blood flow index that changes with time and space by color.

  For example, when the inside of the blood vessel is completely stenotic, the internal pressure at the stenotic site is smaller than the internal pressure at the non-stenotic site. When the internal pressure is designated as the mechanical index, the user can determine the presence or absence of stenosis based on a local color difference on the dynamic model. Further, the flow rate of the stenosis site is smaller than that of the non-stenosis site. When the flow rate is designated as the blood flow index, the user can determine the presence or absence of stenosis based on a local color difference on the dynamic model.

  Further, the vascular stress analysis unit 57 may calculate the spatial distribution of the hardness value as a mechanical index based on the identification result of the parameter of the material model of the plaque region. In this case, the display unit 31 may display the spatial distribution of the hardness value regarding the plaque region on the dynamic model. The display unit 31 may display an internal pressure distribution, a stress distribution, and a strain distribution around the plaque region. The user can use these displays to estimate the nature of the plaque and the likelihood of failure.

  The predicted values of the mechanical index and the blood flow index are not limited only to the method of expressing with the color of the discretized region of the dynamic model. For example, as shown in FIG. 27, FIG. 28, and FIG. FIG. 27 is a graph relating to the blood pressure of the left coronary artery origin. The vertical axis of the graph of FIG. 27 is defined by normalized blood pressure, and the horizontal axis is defined by cardiac phase [%]. FIG. 28 is a graph relating to blood pressure near the branch point between LCX and LDA. The vertical axis of the graph of FIG. 28 is defined by normalized blood pressure, and the horizontal axis is defined by cardiac phase [%]. FIG. 29 is a graph relating to changes in blood pressure with respect to the core line direction. The vertical axis of the graph of FIG. 29 is defined by the blood pressure ratio, and the horizontal axis is defined by the distance [mm] from the aorta. The display unit 31 displays the predicted values of the mechanical index and the blood flow index in a graph so that the user can easily grasp these values.

  When step S11 is performed, the structural fluid analysis process ends.

  In FIG. 20, the forced displacement history is set in the core line portion and the outer wall portion of the shape model. However, the setting location of the forced displacement history is not limited to this. For example, the forced displacement history may be set in a blood vessel wall region between the core line portion and the outer wall portion.

  Further, the assignment target of the constraint condition of the forced displacement history may be divided according to whether the boundary condition and the material model are identified. FIG. 30 is a diagram showing another example of assignment of the forced displacement history, and shows a cross section of the shape model. For example, as shown in FIG. 30A, when a boundary condition and a material model are identified, a forced displacement history is assigned only to the node PN2 on the outer wall portion OW of the shape model, and a forced displacement history is assigned to the node PN3 of the vascular wall region RV. There is no need to assign a displacement history.

  Further, as shown in FIG. 30B, when the boundary condition and the material model are not identified, the forced displacement history may be assigned to both the node PN2 of the outer wall portion of the shape model and the node PN3 of the blood vessel wall region RV. . In this case, a forced displacement history is assigned to the node PN1 on the core line. Further, the node PN2 and the node PN1 of the outer wall portion OW may be connected by the beam element EB, and the forced displacement history may be assigned to the nodes PN2 and PN3 on the beam element EB. At this time, contraction and expansion in the radial direction are expressed by expansion and contraction of the beam element EB. Note that the forced displacement history need not be assigned to the blood vessel lumen region RI.

  FIG. 31 is a diagram showing another example of assignment of forced displacement history, and shows a cross section of a shape model including a dummy element RD of a tissue around a blood vessel. As shown in FIG. 31, the dummy element RD is set outside the blood vessel wall region RN. When the shape model includes the dummy element RD, the node PN4 is set in the dummy element RD in addition to the blood vessel wall region RN. A forced displacement history is also assigned to the node PN4.

  The mechanical model construction unit 55 assigns a forced displacement history to the node PN3 included in the blood vessel wall region RV when identifying the boundary condition and the material model, and does not assign a forced displacement history when the boundary condition and the material model are not identified. Also good. When the forced displacement history is assigned to the node PN3, the material model is identified with reference to the shape index related to the blood vessel wall region RV in addition to the shape index related to the lumen region RI.

  FIG. 32 is a diagram showing another example of assignment of the forced displacement history, and shows a cross section of the shape model including the plaque region RP. As shown in FIG. 32, the plaque region RP is included in the blood vessel wall region RV. The plaque region RP is set in the material model identification region. For the plaque region RP, a material model is identified in consideration of the lumen shape index, the blood vessel wall shape index, and the plaque index.

  As described above, the plaque index is, for example, data relating to the plaque property obtained by the tissue property diagnosis by the ultrasonic diagnostic apparatus. The mechanical model construction unit 55 divides the plaque region into a plurality of partial regions according to the properties, and individually sets the material model identification regions in the plurality of partial regions. It is preferable to set a parameter range in advance for each partial region according to the properties of the partial region. By the above-described statistical identification process, the material model parameters for each partial region are identified by the statistical identification unit 61. In step S10, the display unit 31 displays an index related to the material characteristics of the blood vessel as a mechanical index, so that the user can accurately and easily grasp the property of the plaque.

  Next, a material model that is one of latent variables will be described in detail. As a blood vessel material model, an elastic model, a superelastic model, an anisotropic hyperelastic model, a superelastic model considering viscosity characteristics, and the like are applicable. Examples of anisotropic superelastic models include Y. C. A mathematical model proposed by Fun or a mathematical model called Holzapfel-Gasser constitutive equation can be applied.

  The strain energy per unit reference volume is expressed by the following equation (1). The first term of the formula (1) represents the energy related to the shear deformation of the isotropic basic material not containing collagen. Further, the second term of the formula (1) represents the energy related to the volume deformation of the isotropic basic material not containing collagen. The third term of the formula (1) represents the contribution of each group of collagen fibers (considering the dispersion in the fiber direction).

  FIG. 33 is a diagram illustrating deformation of a fiber group. As shown in FIG. 33, a cylindrical outer membrane is assumed. The deformation of the fiber group in the average direction A on the surface defined by the core line direction z and the circumferential direction θ is expressed by the following equation (2).

  Parameters relating to the material model in the equations (1) and (2) include a material parameter and a fiber dispersion parameter as shown in Table 1 below. C10, D, K1, K2, etc. are used as material parameters, and Kappa, γ, etc. are used as fiber dispersion parameters. The default values and constraint conditions for each parameter are as shown in Table 1.

  As the blood material model, a Casson constitutive expression such as the following expression (3) and an HB constitutive expression such as the expression (4) are suitable.

  The parameters of the material model are identified by the statistical identification unit 61 in the above-described step S6 by statistical identification processing using the blood vessel shape index and the blood fluid index.

  Next, details of the statistical identification processing performed by the statistical identification unit 61 will be described.

  Observation variables such as blood vessel shape indices and blood flow indices measured from time-series medical images have uncertainties. The statistical identification unit 61 uses a statistical method based on a hierarchical Bayesian model and a Markov chain Monte Carlo method as a statistical identification method of a latent variable in a situation where such uncertainty exists.

  As described above, in step S6, the statistical identification unit 61 sets a data distribution based on the predicted value and the observed value of the blood vessel shape index or blood flow index calculated in step S4. The data distribution indicates, for example, a multivariate normal distribution function related to an error between a predicted value and an observed value of a blood vessel shape index or a blood flow index. The data distribution may be set individually for each time or may be set collectively for a plurality of times.

  Next, the statistical identification unit 61 assigns a prior distribution to the forced displacement and the latent variable of the shape model. The prior distribution indicates a probability distribution of possible values. Next, the statistical identification unit 61 executes a parameter survey of a numerical simulation regarding the latent variable, and constructs a model that expresses the relationship between the latent variable and the blood vessel shape index or the blood flow index. For example, the relationship between the material model parameter, the internal pressure distribution parameter, and the blood vessel shape index or blood flow index is defined in the model. The relationship between the blood vessel shape index or blood flow index and the latent variable may be defined by a database or a table instead of a model. These models, databases, or tables are stored in the storage unit 33.

  The statistical identification unit 61 calculates a probability distribution of the blood vessel shape index or the blood flow index from the prior distribution using a model, a database, or a table. The statistical identification unit 61 statistically identifies latent variables from the posterior distribution obtained by the hierarchical Bayesian model and the Markov chain Monte Carlo method.

  Specifically, this identification problem is a defect setting problem that does not satisfy the following three conditions. The three conditions are (1) the existence of a solution is guaranteed, (2) the solution is uniquely determined, (3) the solution continuously changes with respect to the data, and the solution is stable with respect to the measurement error, It is. The defect setting problem is easy to handle when viewed within the framework of normalization theory and its extensions. The standard normalization theory is insufficient to solve the defect setting problem. In order to solve the defect setting problem, a methodology for detecting discontinuities in the internal state and using the detected discontinuous points for estimation of the internal state is required. In this respect, Markov random field theory is effective in the statistical identification processing of the present embodiment.

  In an environment where uncertainty exists in the blood vessel shape index and the blood flow index, the statistical identification unit 61 identifies the probability distribution parameter of the latent variable under an appropriate constraint condition. In order to determine appropriate constraints, it is necessary to know the nature of the solution in advance. The statistical identification unit 61 generates a database related to the constraint condition of the solution space based on the simulation and the observation value.

  The statistical identification unit 61 uses the generated database to execute statistical identification processing based on Markov random field theory and a hierarchical Bayesian model for a super-multi-degree-of-freedom large-scale problem. In the setting of the prior distribution as a constraint condition, the probability distribution of parameters relating to these factors is individually configured in parallel based on many numerical experimental results. The statistical identification unit 61 identifies parameters of latent variables by integrating a plurality of probability distributions and interpolating data loss. For this processing, the statistical identification unit 61 performs estimation by a hierarchical Bayesian method based on a model using Markov random field theory. This is a mechanism that can estimate pressure and flow rate distribution under certain load conditions and boundary conditions based on the identified intermediate variables from the measurement results of the deformation state of the structure to be analyzed.

  The identification problem of material models, boundary conditions, and load conditions in structural fluid analysis of coronary arteries is positioned as nonlinear inverse analysis, and the uniqueness and stability of the solution are often not guaranteed. Since realistically possible ranges of material properties and blood pressure of living tissue can be assumed as a priori information, these can be set as a probability distribution of prior distribution. In addition, since pressure and displacement can be assumed to be spatially and temporally smooth, this information can also be set as a priori probability distribution as a priori information.

  Alternatively, when the fact that no back flow occurs in the blood flow can be taken into account, the fact that the slope of the overall pressure distribution in the blood vessel core direction is negative (there is a pressure drop) may be used as a constraint condition. For load conditions (internal pressure distribution, etc.), boundary conditions, and material model, square error distribution between observed value of blood vessel shape deformation index based on time-series CT image and predicted value of blood vessel shape deformation index based on dynamic model Can be set as the data distribution.

  A square error distribution related to the observable average flow rate may be added as a data distribution. Based on these prior distribution and data distribution, the posterior distribution can be calculated using the hierarchical Bayesian model and the Monte Carlo method. The identification value of the parameter of the latent variable can be obtained by the occurrence probability and variance of the posterior distribution. It can be said that the higher the probability of occurrence and the smaller the variance, the higher the confidence value.

  Even when the posterior distribution is a multimodal distribution, an identification value having a small variance among a plurality of identification values may be selected. Or, when multiple identification values can exist, perform structural fluid analysis under each identification condition, recognize each possibility, and use the identification values and analysis results as guide information for diagnosis and prevention be able to. Since an error is included in the time-series CT image, an error is also included in the blood vessel shape index related to each node of the dynamic model. For this reason, for example, each blood vessel shape index is treated as a normal distribution random variable having an average value of the predicted value of the blood vessel shape index measured from a time-series CT image, and includes a restriction that the spatial order of positions is maintained. In addition, a prior distribution may be set.

  In addition, there are cases where there is no uniqueness in identification of parameters of latent variables, and a plurality of candidates can be considered. In this case, by checking the fluctuation range of the sample set of the identification value of the latent variable with respect to the random sampling point according to the uncertainty of the blood vessel shape index measured from the time-series CT image, the robustness of each identification value candidate is checked. Judgment (stability). The final identification value may be determined based on the robustness of each identification value candidate.

  Next, details of the dynamic model will be described. As described above, the dynamic model construction unit 55 can construct different types of dynamic models depending on the type of the dynamic model. When FEM based on continuum mechanics is used, the mechanical model construction unit 55 includes both a shape model for stress analysis of the blood vessel wall (FEM model) and a shape model for fluid analysis of blood (FEM model). Build up.

  When a simple identification method based on material mechanics is used, the relationship between pressure, elastic modulus, and displacement is approximately obtained from the equation of a thick cylinder that receives internal pressure in material mechanics. In this case, thick cylindrical approximation is used as a shape model for each of a plurality of discretized regions arranged in the core line direction. Specifically, the dynamic model construction unit 55 identifies the blood vessel lumen shape, the blood vessel wall surface shape, and the cross-sectional center on the cross section passing through the nodes discretely arranged on the core line.

  Next, the dynamic model construction unit 55 calculates the average area, the average radius of the lumen, and the average wall thickness based on the blood vessel lumen shape and the blood vessel wall surface shape. Based on the average area, the average radius of the lumen, and the average wall thickness, the dynamic model construction unit 55 constructs a shape model by performing thick-walled cylinder approximation on the blood vessel region of each discretization region.

  When a simple identification method based on flow mechanics is used, the modified Bernoulli equation in fluid mechanics or the Hagen-Poiseille Flow is used to approximate the mean blood pressure and mean flow. Use the formula. In this case, a shape model for approximating the relationship between the blood pressure difference and the flow rate of each of the plurality of discretized regions is constructed. Here, the blood pressure difference is a pressure difference between the inlet blood pressure and the outlet pressure, and the flow rate means the inlet flow rate (or flow velocity) and the outlet flow rate (or flow velocity) per unit time. However, the dynamic model construction unit 55 associates the movement vector for each node with the rotational displacement for the node adjacent to the node at each time based on the expansion / contraction distance and the twist angle in the core line direction calculated in step S2. And good.

  Next, structural fluid analysis using a continuum mechanics model will be described.

  The kinematics for blood vessels and blood (substances that make up blood vessels and blood) are independent of the forces causing that motion. The basic concept of kinematics for blood vessels and blood is an abstraction of the intuitive notion of position, time, objects, motion, and a collection of deformable substances into mathematical terms. The basic kinematic tensors that govern the local analysis of deformation and motion for blood vessels and blood are the deformation gradient tensor F and the velocity gradient tensor L.

  The deformation gradient tensor F defines the size and shape changes that occur in the moving blood vessels and blood material elements. The deformation gradient tensor F is represented by the product of a rotation tensor (strict orthogonal tensor) R and a stretching tensor (positive symmetric tensor) U and V. The stretching tensors U, V are brought about by first imposing a stretch on the direction defined by the orthonormal vector R in the basic form and then imposing a rigid body rotation given by the orthonormal vector R. Note that the order of imposing stretch and the order of imposing rigid body rotation may be reversed.

  The velocity gradient tensor L does not depend on the reference form but depends only on the current form. The velocity gradient tensor L defines the rate at which the size and shape changes that occur in the moving blood vessels and blood material elements occur. The velocity gradient tensor L can be separated into a strain rate tensor D (symmetric tensor) and a spin tensor (antisymmetric tensor). The strain rate tensor D represents the rate of change of the stretch when the object just passes through its current form. The spin tensor represents the rate of change of rotation when an object just passes through its current form.

  Displacement constraint conditions (including temporal changes) are assigned to the deformation gradient tensor and velocity gradient tensor relating to a part of the outer surface node (or integration point) of the mechanical model of the blood vessel. Predicted values (deformation gradient tensor, velocity gradient tensor, or their function values (for example, displacement or area)) and observation values (deformation obtained from observation data) for the lumen node (or integration point) of the dynamic model The material model parameters, loading conditions (surface force vector in the lumen of the dynamic model), and boundary conditions (force vector at the vessel boundary) are matched so that the gradient tensor, velocity gradient tensor, or their function values match. Identify. Here, the initial state of the stress inside the blood vessel may be assumed in advance or may be identified.

  Dynamic models based on continuum mechanics are based on equations representing the balance of mass, kinematics, momentum, angular momentum, and energy in moving blood vessels and blood. The concepts of mass, force, heat, and internal energy are fundamental. The balance law is that the momentary total derivative of momentum is equal to the sum of object force and contact force, the momentary total derivative of angular momentum is equal to the sum of object torque and contact torque, and kinetic energy and internal It means that the change with time in energy is equal to the sum of power (mechanical energy), heat supply per unit time and heat flux.

  From balance laws, constitutive equations, and jump conditions, field equations such as deformation gradient tensors, velocity gradient tensors, and stress tensors can be derived to describe vascular and blood dynamic models. In addition, the strain fields in blood vessels and blood satisfy the fitting conditions.

  Here, the constitutive equation gives a relationship of a set of 10 scalar equations composed of density, internal energy, velocity vector, stress tensor, heat flux vector, and temperature. Of the 17 scalar fields in the field balance equation, ie, density, internal energy, velocity vector, stress tensor, and heat flux vector, the field balance gives 8 scalar relationships and added the temperature Give the remaining unknown relationships. However, the object force b and the heat source r are known. The parameters of the equations giving these scalar fields are material model parameters.

  The mechanics model based on continuum mechanics is a numerical analysis method using the finite element method or the boundary element method, and approximates the field such as displacement vector, stress tensor, strain tensor, and velocity vector, given boundary condition, load condition, And can be calculated based on the material model.

  In the structure-fluid coupled analysis, the method of solving the structure and fluid equations may be either a monolithic method or a partitioned method. Further, the coupling at the interface between the structure and the fluid may be weakly coupled or strongly coupled. Further, in fluid analysis, a moving boundary may be handled by a boundary surface tracking type technique represented by the ALE method, or “Immersed Boundary method”, “Immersed Finite Element Method”, or “Fittious Domain Method”. A boundary surface supplement type technique such as the above may be used.

  Next, as an example of a simple dynamic model, the material dynamics equation of the thick cylinder subjected to internal pressure and external pressure, the Hagen-Poiseuille flow and the modified Bernoulli equation will be described in detail.

  First, an equation of material dynamics of a thick cylinder will be described with reference to FIG. 34 and FIG. FIG. 34 is a diagram showing an orthogonal cross section of a dynamic model of a thick cylinder. FIG. 35 is an enlarged view of the minute sector element of FIG. Expressions such as stress, strain, and displacement when the internal pressure pa and the external pressure pb are applied to a thick cylinder having the inner radius ra and the outer radius rb will be described. E and ν are material model parameters. E represents the elastic modulus, and ν represents the Poisson's ratio. For thick cylinders, it is necessary to consider the radial stress σr and the radial distribution of the circumferential stress σθ. Hereinafter, the axial strain εz is assumed to be uniform with respect to the position and orientation of the cross section.

  Moreover, although the case where a cylindrical cross section is axially symmetrical is demonstrated, arbitrary shapes may be sufficient. When the cylindrical cross section is axisymmetric, the equilibrium condition may be considered only in the radial direction of an arbitrary cross section. Consider a balance of forces in the radial direction on a concentric cylinder with radii r and r + dr on a given cross section and a small sector element with unit thickness 1 cut at a central angle dθ. Since the deformation is also axisymmetric, no shear stress is generated on the ab plane and the bc plane, so that only normal stress acts. Therefore, the force balance in the radial direction can be expressed as the following equation (5).

  σr rdθ + 2σθr sin (dθ / 2)-(σr + (dσr / dr) dr) (r + dr) dθ = 0 (5)

  Here, since dr is smaller than r and dσr is smaller than σr, if high-order minute terms included in equation (5) are omitted and sin (dθ / 2) ≈dθ / 2, then equation (5) Can be expressed as the following equation (6).

  rdσr / dr + σr-σθ = 0 (6)

  If the radial displacement at the radius r is u, the displacement in the same direction at u + dr is u + (du / dr) dr, so the radial strain εr at the radius r is εr = du / dr. Further, due to the radial displacement u, the circle with the radius r becomes a circle with the radius r + u. Therefore, the circumferential strain εθ can be expressed as the following equation (7).

  εθ = (2π (r + u) -2πr) / 2πr = u / r (7)

  Moreover, the following (8) Formula or (9) Formula is obtained from the relational expression of stress and strain.

  d2u / dr2 + (1 / r) (du / dr) -u / r2 = 0 (8)

  d2u / dr2 + d (u / r) / dr = 0 (9)

  The following formula (10) can be obtained by integrating the formula (8) or the formula (9).

  u = c1r + c2 / r (10)

  As a result, the following equations (11), (12), and (13) are obtained.

  σr = (E / ((1 + ν) (1-2ν))) (c1- (1-2ν) (c2 / r2) + νεz) (11)

  σθ = (E / ((1 + ν) (1-2ν))) (c1 + (1-2ν) (c2 / r2) + νεz) (12)

  σz = (Eν / ((1 + ν) (1-2ν))) (2c1 + ((1-ν) / ν) εz) (13)

  The constants c1 and c2 in the equations (11), (12), and (13) are determined from the peripheral conditions, that is, σr = −pa at the inner circumference r = ra of the cylinder and σr = −pb at the outer circumference r = rb. be able to. Under these peripheral conditions, the following expressions (14), (15), and (16) can be obtained from the expressions (11), (12), and (13), respectively. Further, the displacement u can be expressed as the following equation (17).

  σr = (1 / (rb2-ra2)) (ra2 (1-rb2 / r2) pa-rb2 (1-ra2 / r2) pb) (14)

  σθ = (1 / (rb2-ra2)) (ra2 (1 + rb2 / r2) pa-rb2 (1 + ra2 / r2) pb) (15)

  σz = 2ν (ra2pa-rb2pb) / (rb2-ra2) + Eεz = ν (σr + σθ) + Eεz (16)

  u = ((1 + ν) (1-2ν) / E) ((ra2pa-rb2pb) / (rb2-ra2)) r + ((1 + ν) / E) ((ra2rb2) / ((rb2-ra2) r)) (pa-pb) -νεzr (17)

  Expressions (16) and (17) include the term εz. Therefore, equation (16) varies depending on the constraint condition at the boundary of the target cylinder. For example, when both ends of the target cylinder are constrained, εz = 0, both ends are opened, and σz = 0.

  The shear stress τr in the rθ plane can be expressed as the following equation (18).

  τr = (1/2) (σr−σθ) = ((ra2rb2) / ((rb2-ra2) r2)) pb (18)

  Since the shear stress τr ′ in the θz plane becomes maximum when σz = 0, when σz = 0, it can be expressed as the following equation (19).

  τr ′ = (1/2) | σθ | = (1/2) ((rb2) / (rb2-ra2)) (1 + ra2 / r2) pb (19)

  Next, the Hagen-Poiseuille flow and the modified Bernoulli equation will be described. When the fluid flows into the circular pipe, the pressure decreases as it proceeds downstream, and the flow velocity distribution also gradually changes. When the flow flows into the pipe, a boundary layer develops from the pipe wall, and as the flow proceeds downstream, the boundary layer increases in thickness, and finally the flow in the pipe is covered with the boundary layer. For this reason, the velocity distribution changes from a substantially flat distribution at the pipe inlet to a downstream parabolic distribution, and the velocity distribution thereafter does not change. This state is called a fully developed flow, and the pressure drop due to pipe friction loss is also a constant rate. The section where the flow reaches the flow developed from the pipe entrance is called the run-up section or the entrance section, and the length of the section is called the run-up distance or the entrance length. Since the velocity distribution of the fully developed flow does not change in the downstream direction, as shown in the following equation (20), the acting force of the pressure loss ΔP caused by the pipe friction loss and the friction force of the shear stress τ caused by the viscosity of the fluid Will be balanced. In the case of a flow in a circular pipe, it is said that the flow becomes laminar when the Reynolds number Re is about 2300 or less.

  In the case of laminar flow, the velocity distribution u is represented by an axisymmetric rotational paraboloid as shown in the following equation (21).

  The flow rate Q can be expressed as the following equation (22) by integrating the velocity distribution u over the entire pipe cross section.

  The cross-sectional average flow velocity v can be expressed as the following equation (23).

The pressure gradient is constant in the flow direction and the pressure decreases. The pressure gradient can be expressed as the following equation (24) using the pressure drop Δp of the tube length l. Substituting equation (22) into equation (24) yields equation (25). Equation (25) means that the flow rate Q is proportional to the pressure loss Δp. The flow that satisfies this relationship is called the Hagen-Poiseuille flow.

  The modified Bernoulli equation when there is a loss can be expressed as the following equations (26) and (27).

  Here, l is the tube length, d is the tube inner diameter, v is the tube average velocity, and λ is the tube friction coefficient. The pipe friction coefficient λ is a value determined by the Reynolds number Re when the flow is laminar and by the Reynolds number Re and the surface roughness when the flow is turbulent. Frictional resistance is always affected by the viscosity of the fluid. This frictional resistance is energy loss because it consumes the power or energy that drives the flow.

  When the Hagen-Poiseuille flow equation is transformed to the Darcy-Weissbach equation, the following equation (28) relating to the pressure difference, flow velocity, and pipe inner diameter is obtained.

  This completes the description of the Hagen-Poiseuille flow and the modified Bernoulli equation.

  As described above, the analysis device 27 can identify a latent variable using the material dynamics equation and the Hagen-Poiseuille flow and modified Bernoulli equations. For example, as a dynamic model, consider a case in which the deformation of a blood vessel is expressed by an equation of material dynamics of a thick-walled tube, and the change in the tube diameter is expressed by an internal pressure change and an elastic modulus.

  Assuming that the stress-free state is the initial shape (for example, the state in which the blood vessel contracts the most), if the elastic modulus of the blood vessel wall and plaque is set to a certain value, the observed value of the blood vessel shape deformation index such as the average radius of the blood vessel lumen A relational expression between the amount of change in time and the amount of change in internal pressure is obtained. The observed value of the blood vessel shape deformation index is measured from a time-series CT image. The temporal change of the internal pressure distribution of the blood vessel is determined so as to match the temporal change amount of the observed value of the blood vessel shape deformation index. The predicted value of the blood vessel flow rate index is measured by performing blood fluid analysis under this internal pressure distribution. If the predicted value of the blood vessel flow rate index does not match the observed value, the analysis device 27 changes the initially determined elastic modulus of the blood vessel wall or plaque and performs the same analysis.

  By repeating this, the analysis device 27 causes latent variables such as the elastic modulus of the blood vessel wall and plaque, the internal pressure distribution, the pressure boundary condition of the fluid analysis, and the like that match the observed value of the blood vessel shape deformation index and the observed value of the blood flow index. Can be determined. In order to perform this determination method more efficiently and stably, a statistical identification method using a hierarchical Bayesian model and a Markov chain Monte Carlo method may be used.

  As described above, the blood vessel analysis device 50 includes the storage unit 33, the region setting unit 51, the image analysis / tracking processing unit 53, the dynamic model construction unit 55, and the statistical identification unit 61. The storage unit 33 stores time-series medical image data related to the blood vessels of the subject. The region setting unit 51 sets an analysis target region in a blood vessel region included in a time-series medical image. The image analysis / tracking processing unit 53 performs image processing on the time-series medical image and calculates a time-series shape index and a time-series shape deformation index of the analysis target region.

  The mechanical model construction unit 55 provisionally constructs a mechanical model related to the structural fluid analysis of the analysis target region based on the time-series form index, the time-series shape deformation index, and the time-series medical image. The statistical identification unit 61 analyzes the region to be analyzed so that the predicted values of the blood vessel shape index and the blood flow rate index based on the tentatively constructed dynamic model match the pre-measured values of the blood vessel shape index and the blood flow rate index. To identify the latent variables of the dynamic model.

  With the above configuration, the blood vessel analysis device 50 according to the embodiment identifies latent variables such as a material model, boundary conditions, load conditions, and a geometric structure by inverse analysis using a blood vessel shape deformation index and a blood flow index. be able to.

  The blood vessel analysis device 50 repeatedly performs the inverse analysis while changing the latent variable, thereby achieving the above four points of difficulty, namely, 1. 1. Identification method of coronary artery material model 2. Incorporating the effects of heart shape deformation on coronary arteries. 3. Identification method of coronary artery boundary condition; It is possible to identify a latent variable in consideration of all of a material model using a blood vessel shape having uncertainty, a load condition, and a boundary condition identification method.

  Therefore, the blood vessel analysis device 50 can perform structural fluid analysis in consideration of the influence of external factors such as blood vessels and the heart that are not depicted in the CT image. Thus, according to the medical image diagnostic apparatus of the embodiment, the accuracy of the structural fluid analysis of blood vessels can be improved.

  Then, the blood vessel analysis device 50 constructs a final dynamic model by assigning the identified latent variables to the shape model. Furthermore, the blood vessel analyzing apparatus 50 identifies the material characteristics (material model, material parameter) at each position of the blood vessel of the subject by analyzing the constructed dynamic model. Then, the identified material characteristics are output to the processing device 70.

  Although the embodiment has been described above, the embodiment and the modification are presented as examples, and are not intended to limit the scope of the invention. These novel embodiments and modifications can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. The embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

27 analysis device 29 input unit 31 display unit 33 storage unit 50 blood vessel analysis device 70 processing device 70A first acquisition unit 70B second acquisition unit 70C construction unit 70D first generation unit 70E first control unit 70F reception unit 70G second generation unit 70H Second control unit 70I Third control unit 72 Modeling unit

Claims (11)

A first acquisition unit that acquires an image related to a blood vessel of a subject;
A second acquisition unit for acquiring a material property of a predetermined position of the blood vessel;
From the image, a construction unit for constructing a shape model indicating the three-dimensional shape of the blood vessel,
A first generation unit that generates a blood vessel model in which additional information related to the material property is added to the predetermined position of the blood vessel indicated by the shape model;
A processing apparatus comprising:   The processing apparatus according to claim 1, wherein the material property indicates at least one of elastic modulus, flexibility, strength, hardness, and toughness. The first generator is
The blood vessel model to which the additional information obtained by correcting the relative position of the outer wall with respect to the inner wall of the blood vessel wall is added so that the thickness of the blood vessel wall indicated by the blood vessel model is a thickness according to the material characteristics of the blood vessel. Generate,
The processing apparatus according to claim 1. The first generator is
Generating the blood vessel model to which the additional information indicating the type of the modeling material of the blood vessel model is added so that the modeling material of the blood vessel model corresponding to the blood vessel model satisfies the corresponding material characteristics;
The processing apparatus according to claim 1. The first generator is
The blood vessel to which the additional information indicating the application position of a reinforcing member that reinforces the blood vessel indicated by the blood vessel model from the outside is added so that the mechanical properties of the blood vessel model corresponding to the blood vessel model satisfy the corresponding material properties Generate a model,
The processing apparatus according to claim 1. A first control unit that displays a blood vessel image indicated by the shape model on a display unit;
A reception unit for receiving instructions from the user;
A second generation unit that generates an output signal having an intensity corresponding to a material characteristic indicated by the additional information corresponding to the indicated position in the displayed blood vessel image;
A second control unit that controls an input unit operated by a user so as to generate a stress corresponding to the intensity of the output signal;
Further comprising
The processing apparatus according to claim 1. In order to model a blood vessel model corresponding to the blood vessel model, further comprising a third control unit that controls a modeling unit that models a three-dimensional modeled object,
The processing apparatus according to claim 1. A blood vessel model simulating the blood vessel of a subject,
A blood vessel model having an inner diameter corresponding to the blood vessel of the subject and a relative position of the outer wall with respect to the inner wall of the blood vessel being a position where the material characteristics of the blood vessel of the subject can be realized. Obtaining an image of a blood vessel of a subject;
Obtaining material properties of a predetermined location of the blood vessel;
Constructing a shape model indicating the three-dimensional shape of the blood vessel from the image;
Generating a blood vessel model in which additional information related to the material property is added to the predetermined position of the blood vessel indicated by the shape model;
An image processing method including: Obtaining an image of a blood vessel of a subject;
Obtaining material properties of a predetermined location of the blood vessel;
Constructing a shape model indicating the three-dimensional shape of the blood vessel from the image;
Generating a blood vessel model in which additional information related to the material property is added to the predetermined position of the blood vessel indicated by the shape model;
A program that causes a computer to execute. Modeling for modeling a blood vessel model corresponding to a blood vessel model in which additional information related to material characteristics of the blood vessel at the predetermined position is added to a predetermined position of the blood vessel indicated by a shape model indicating the three-dimensional shape of the blood vessel of the subject. And
A modeling apparatus.