JP6362851B2 - Blood vessel analysis device, blood vessel analysis program, and blood vessel analysis device operating method - Google Patents

Description

Embodiments of the present invention, vessel analysis apparatus, vessel analysis program, and a method of operating vascular analyzer.

  Technology for preventing and diagnosing non-invasive or minimally invasive stenosis of coronary arteries, cerebral aneurysms, or plaques of carotid arteries that are the precursors of heart disease, one of the three major diseases Is desired.

  Coronary stenosis is a critical lesion leading to ischemic heart disease. As a diagnosis of coronary stenosis, coronary angiography (CAG) using a catheter is the mainstream. As a diagnostic index of an organic lesion of the coronary artery, there is a myocardial blood flow reserve ratio (FFR: Fractional Flow Reserve). FFR is defined as the ratio of the maximum coronary blood flow in the presence of stenosis to the maximum coronary blood flow in the absence of stenosis. The FFR substantially matches the ratio of the stenotic distal coronary pressure to the proximal stenotic coronary pressure. A pressure sensor provided at the catheter tip is measured. That is, the measurement of FFR requires catheter surgery.

  If structural fluid analysis of coronary arteries is possible with cardiac CT, it is possible to save minimally invasive, reduce patient burden, and save medical costs compared to FFR measurement by catheter surgery. However, in cardiac CT, only an index based on the size of a plaque region or a thrombus region included in a CT image can be measured non-invasively. If the pressure difference before and after the thrombus can be measured by structural fluid analysis based on the CT image, the effect of the thrombus (or plaque) can be quantified.

  Clinical evaluation of coronary circulation dynamics includes ultrafast CT, cineangiogram, ultrasound, nuclear medicine imaging including SPECT (single photon emission tomography) and PET (positron emission tomography), MRI (nuclear magnetic resonance) Imaging method) has been developed and introduced, which is useful for diagnosis and evaluation of treatment methods.

  However, it is difficult to accurately capture coronary microvessels with a medical image diagnostic apparatus. Further, even if the blood vessel shape is clear, there are many cases where the medical image includes noise or there is ambiguity in the threshold setting of the boundary of the living tissue. As described above, the blood vessel shape obtained from the medical image diagnostic apparatus has uncertainty.

  When utilized in clinical applications, analysis is often performed only on the thick region of the coronary artery from the aortic root upstream of the coronary microvessel. Since coronary blood flow greatly affects coronary microvascular tonicity (tonus), it is possible to appropriately set boundary conditions for fluid analysis such as flow rate or pressure at the exit of coronary artery in the thick region or rate of change thereof It becomes a problem. The blood flow in the coronary arteries is subjected to mechanical factors (total movement due to pulsation, local expansion / contraction, torsion, forced displacement or external force due to shear deformation) due to the pulsation of the heart. The fluid analysis alone cannot take into account the influence of mechanical factors such as the pulsation of the heart, and therefore cannot accurately measure the blood flow distribution and internal pressure distribution. On the other hand, a structure-fluid interaction analysis that considers the influence of mechanical factors has been performed on the heart and vascular system captured by images. However, even when a structure-fluid coupled analysis is performed, it is often difficult to correctly set the boundary condition of the blood vessel inlet and outlet and the blood vessel and plaque material model in the blood (including the contrast medium) fluid analysis. . In addition, when there are micro blood vessels that are not depicted in the image, the influence of the micro blood vessels on the blood flow may not be considered. For this reason, the analysis result of the structure-fluid coupling analysis may not reproduce the actual blood flow and blood vessel deformation. In addition, when boundary conditions, load conditions, and material models are not appropriate, or when a blood vessel is accompanied by a large movement, there may be a problem in convergence and analysis stability. As described above, the conventional structural fluid analysis of blood vessels may require a lot of analysis resources and analysis time, and the error of the analysis result may increase. Problems may arise.

US Pat. No. 8,815,774 Kadooka et al. (ITU Journal, Tailor-made Medicine Developed by the Heart Simulator-Introduction of the World's Most Advanced Heart Simulator and its Applications-, Vol. 41, No. 6)

The purpose of the embodiment is based on image analysis and structural fluid analysis of blood vessels (including blood), and blood vessel analysis that enables highly accurate analysis of stenosis indices such as changes in blood flow and pressure loss due to stenosis device is to provide vascular analysis program, and a method of operating a blood tube analyzer.

According to the embodiment, medical image data over a plurality of time phases related to a blood vessel of a subject into which a contrast agent has been injected is stored in the storage unit. An analysis target region is set in a blood vessel region included in a medical image over a plurality of time phases. Morphology index over a plurality of time-position phase of a plurality of time medical image imaging to the analysis target region spreading phase, the shape deformation index, and CT value change index is calculated by the image processing unit. The image processing unit forms an index further over a plurality of time phases, deformation index, and CT due to flow rate change of the contrast medium in the local vascular region surrounding the target site contained in the analysis target area based on the CT value change index A value change index and a blood vessel cross-sectional shape deformation index in the local blood vessel region are calculated. A stenosis index relating to the target region is calculated based on the CT value change index and the blood vessel cross-sectional shape deformation index in the local blood vessel region.

It is a figure which shows the outline | summary of the stenosis parameter | index analysis method concerning this embodiment. It is a figure which shows the relationship of the parameter in connection with the blood-flow phenomenon of the blood vessel stenosis used with the stenosis parameter | index analysis method concerning this embodiment. It is a figure which shows the example of the model base which should be prepared before the stenosis identification by the stenosis parameter | index analysis method concerning this embodiment. 1 is a diagram showing a schematic block configuration of a medical image diagnostic apparatus (X-ray computed tomography apparatus) according to the present embodiment. It is a figure which shows an example of the specific identification method of the parameter required for the structural fluid analysis which concerns on this embodiment. It is a figure which shows an example of the observation data and estimation data in the structural fluid analysis which concerns on this embodiment. It is a figure which shows the typical flow of the structural fluid analysis process performed under control of the system control part of FIG. It is a figure which shows an example of the specific flow of the structural fluid analysis processing procedure of FIG. 7 from a process and a data type. It is a figure which shows the other example of FIG. It is a figure which shows the structural fluid analysis processing procedure of FIG. 7 more concretely. The figure which shows an example of the dynamic model regarding the object area | region of the structural fluid analysis which concerns on this embodiment. It is a figure for demonstrating the posteriori distribution calculation regarding the load conditions (average pressure in a blood vessel) by the hierarchical Bayes model and the Markov chain Monte Carlo method, and identification of an average internal pressure in the structural fluid analysis process concerning this embodiment. It is a figure for demonstrating the posterior distribution calculation regarding the material model parameter by the hierarchical Bayes model and the Markov chain Monte Carlo method, and the identification of the material model parameter (equivalent elastic modulus of the blood vessel wall) in the structural fluid analysis processing according to the present embodiment. .

  As shown in FIG. 1, the blood vessel analyzing apparatus according to the present embodiment includes a two-dimensional array type detector in which detection elements are arranged vertically and horizontally, and a plurality of detectors and X-ray tubes are continuously rotated and continuously irradiated. Analyzing multi-phase medical image data (1) relating to blood vessels (including blood) of a subject injected with a contrast agent by so-called 4D-CT that generates volume data over time phases (hereinafter simply referred to as medical images). Then, extraction of blood vessel wall coordinate change, CT value change of specific blood vessel region, cross-sectional shape change, structure / fluid analysis condition of blood vessel (including stenosis) and blood flow (including contrast medium) (2) And a stenosis index (3) such as pressure change before and after the stenosis, flow rate change, and contrast agent concentration change can be analyzed.

  That is, in the present embodiment, the analysis accuracy is improved by identifying the boundary condition of the stenosis region in consideration of the concentration gradient (4) of the contrast agent when performing the structural fluid analysis.

  The blood vessel analysis device according to the present embodiment is characterized in that the change in the cross-sectional shape of the blood vessel and the change in the CT value show characteristic changes before and after the stenosis part, depending on the magnitude of the influence of blood flow inhibition by the stenosis part. Pay attention.

Here, FIG. 2 shows the relationship of parameters related to the blood flow phenomenon of vascular stenosis. In the structure / fluid analysis condition identification process, the boundary conditions (flow rate and pressure) of the target blood vessel inlet and outlet, the material model parameters (elastic modulus) of the blood vessel wall, the material model parameters (elastic modulus) of the stenosis, and the blood vessel wall It is characterized by identifying a shape parameter (an unstressed blood vessel shape) and a relationship model parameter between a CT value and a flow rate. Since the CT value distribution, the blood vessel cross-sectional shape, the contrast medium concentration, and the flow velocity distribution change depending on each other, the feature is that the parameters are identified in consideration of these relational model parameters .
As shown in FIG. 2, in order to calculate a stenosis index such as pressure loss before and after stenosis, pressure ratio, flow rate ratio, and contrast medium concentration gradient, three relational models are calculated: CT in the vasodilation state, the contraction state, and the intermediate state Prepare by utilizing vascular stress analysis and blood flow analysis using vascular shape, vascular cross-sectional shape deformation, CT value change data by image analysis and image tracking.

Relationship model Relationship model of blood vessel cross-sectional shape deformation 51, internal pressure 52, wall thickness 54, unstressed cross-sectional shape 53, and vascular material constitutive equation 55; Func1
Relational model 2. Relationship model of pressure loss 61 before and after blood vessel stenosis, flow velocity 62, cross-sectional shapes 63 and 64, and blood material constitutive equation 65;
Relational model 3. Relational model of CT value 71, cross-sectional shape 72, flow velocity 73, contrast agent concentration 74, and contrast agent material constitutive formula 75; Func3
These relationship models are expressed by response surface models (for example, mathematical expressions such as polynomials) between the parameters.

  For example, as illustrated in FIG. 3, the relationship model 1 is the deformation amount of the blood vessel cross section diameter = Func1 (pressure, vascular elasticity, Poisson's ratio, unstressed diameter, initial wall thickness), and the relationship model 2 is the vascular stenosis. Before and after pressure loss = Func2 (flow velocity, stenosis diameter, blood vessel shape), relational model 3 includes CT value = Func3 (flow velocity, vessel cross-sectional diameter, contrast agent concentration).

  Here, the positions before and after the stenosis are expressed as A before the stenosis and B after the stenosis. In vascular stress analysis, stress analysis is performed including the region before A before stenosis and the region after B after stenosis. On the other hand, in the blood flow analysis, when the inlet boundary condition is a pressure condition, when the inlet is a pressure condition, the cross section of A before stenosis is taken as the inlet, and when the outlet boundary condition is a pressure condition, The section of B after stenosis is the exit. If the inlet boundary condition is a flow rate or flow velocity distribution condition, include the region before A before stenosis, or if the outlet boundary condition is a flow rate or flow velocity distribution condition, include the region after B after stenosis. Blood flow analysis.

  Hereinafter, a blood vessel analysis device and a blood vessel analysis program according to the present embodiment will be described with reference to the drawings.

  The blood vessel analysis device according to the present embodiment is a computer device for analyzing a structural fluid of a blood vessel region included in a medical image generated by a medical image diagnostic device. The blood vessel analyzing apparatus according to the present embodiment may be incorporated in a medical image diagnostic apparatus, or may be a computer apparatus such as a workstation separate from the medical image diagnostic apparatus. Hereinafter, it is assumed that the blood vessel analyzing apparatus according to the present embodiment is incorporated in a medical image diagnostic apparatus for specific description.

  The medical image diagnostic apparatus according to this embodiment can be applied to any type of image diagnostic apparatus equipped with an imaging mechanism for scanning a subject. As a medical image diagnostic apparatus according to the present embodiment, for example, an X-ray computed tomography apparatus (X-ray CT apparatus), a magnetic resonance diagnostic apparatus, an ultrasonic diagnostic apparatus, a SPECT apparatus, a PET apparatus, a radiotherapy apparatus, etc. are used as appropriate. Is possible. Hereinafter, it is assumed that the medical image diagnostic apparatus according to the present embodiment is an X-ray computed tomography apparatus for specific description.

  FIG. 4 is a schematic block diagram of a medical image diagnostic apparatus (X-ray computed tomography apparatus) according to this embodiment. As shown in FIG. 4, the X-ray computed tomography apparatus includes a CT mount 10 and a console 20. The CT gantry 10 scans the imaging region of the subject with X-rays according to the control from the gantry controller 23 of the console 20. The imaging site is typically 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, an image processing device 27, an input device 29, a display device 31, and a storage device 33 with the system control unit 21 as a center. The gantry control unit 23 controls each device in the console 20 in accordance with the scan condition set by the user via the input device 29. The reconstruction device 25 generates CT image data 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 image reconstruction processing on the projection data set to generate CT image data as volume data. Image reconstruction algorithms include analytical image reconstruction methods such as FBP (filtered back projection) method, ML-EM (maximum likelihood expectation maximization) method, OS-EM (ordered subset expectation maximization) method, etc. Existing algorithms such as image reconstruction can be employed. In the present embodiment, the reconstruction device 25 generates time-series CT image data based on the time-series projection data set. The CT image includes a pixel region related to a blood vessel contrasted with a contrast agent (hereinafter referred to as a blood vessel region). 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. Time-series CT image data is stored in the storage device 33.

  The image processing device 27 constructs a dynamic model based on time-series CT images and executes structural fluid analysis. Details of the processing of the image processing device 27 will be described later. The input device 29 receives various commands and information input from the user. As the input device 29, a keyboard, a mouse, a switch, or the like can be used. The display device 31 displays various information such as CT images and structural fluid analysis results. As the display device 31, for example, a CRT display, a liquid crystal display, an organic EL display, a plasma display, or the like can be used as appropriate. The storage device 33 is composed of various storage media such as a hard disk device. The storage device 33 stores various data such as time-series projection data and time-series CT image data for a subject into which a contrast medium has been injected. For example, the storage device 33 stores time-series CT image data in a medical image file format conforming to the DICOM (digital imaging and communications in medicine) standard. The storage device 33 may store medical data collected by an external device in association with time-series CT image data in a medical image file.

  The system control unit 21 includes a central processing unit (CPU), a read only memory (ROM), and a random access memory (RAM). The system control unit 21 functions as the center of the X-ray computed tomography apparatus. The system control unit 21 executes a blood vessel structure analysis process according to the present embodiment by executing a blood vessel analysis program stored in the ROM or RAM.

  The system control unit 21, the image processing device 27, the input device 29, the display device 31, and the storage device 33 constitute a blood vessel analysis device 50. As in the present embodiment, 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 receives medical data such as time-series CT images from the medical image diagnostic device or PACS (picture archiving and communication systems) via a network. Collect it.

  Next, an example of an operation according to the present embodiment will be briefly described with reference to FIG. Note that the blood vessel analysis device according to the present embodiment can analyze blood vessels in all parts of the human body such as cardiovascular, carotid artery, and cerebral artery. However, in the following, for the sake of specific description, it is assumed that the analysis target according to the present embodiment is a blood vessel of the heart. Briefly describing the blood vessels of the heart, examples of the blood vessels 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 guaranteed 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. It is known that retrograde waves are observed during systole in hypertrophic cardiomyopathy and aortic stenosis, and specific blood flow waveforms are exhibited depending on the disease, such as an increase in the systolic forward wave in aortic regurgitation. 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 may 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 branching anatomically 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.

Next, a specific method for identifying parameters necessary for structural fluid analysis will be described with reference to FIG. The flow of the identification method is as follows. The following is obtained from the cross-sectional diameters d _in1 , d _in2 , d _in3 before stenosis and the cross-sectional diameters d _out1 , d _out2 , d _out3 after stenosis in the vasoconstriction state 1, the expansion / contraction intermediate state 2, and the expansion state 3.
P _in1 = Func1 (d _in1 , E, d ₀ )
P _in2 = Func1 (d _in2 , E, d ₀ )
P _in3 = Func1 (d _in3 , E, d ₀ )
P _out1 = Func1 (d _out1 , E, d ₀ )
P _out2 = Func1 (d _out2 , E, d ₀ )
P _out3 = Func1 (d _out3 , E, d ₀ )
0 = Func1 (d ₀ , E, d ₀ )
Where E is the stiffness parameter of the blood vessel and d ₀ is the unstressed diameter.
ΔP ₁ = P _in1 − P _out1
ΔP ₂ = P _in2 − P _out2
ΔP ₃ = P _in3 − P _out3
If the lumen shape parameter of the stenosis is d _p
ΔP ₁ = Func2 (v ₁ , d _p )
ΔP ₂ = Func2 (v ₂ , d _p )
ΔP ₃ = Func2 (v ₃ , d _p )
Ask for.

If the CT value at flow rate 0 is CT0, hierarchical Bayes such that v1, v2, v3 and CT1-CT0, CT2-CT0, CT3-CT0 satisfy the Func3 relationship from the CT values in states 1, 2, and 3. Parameters E, d ₀ , and d _p are obtained by an algorithm. From the relationship between d _p obtained in each of states 1, 2, and 3 and the pressure distribution, the hardness parameter E _p of the stenosis is obtained by structural inverse analysis.

  The above three response surface models (relational equations for each parameter) are created by structural / fluid analysis based on CT image processing data during expansion, contraction, and intermediate states, pressure loss, vessel hardness (elastic modulus) Using the flow rate as an estimated value and the diameter of the constriction and the stress-free state as parameters, the variable that cannot be observed is obtained from the variable that can be observed by the hierarchical Bayes & Markov chain Monte Carlo method.

  In these structural fluid analyses, the accuracy of the analysis is improved by identifying the boundary condition of the stenosis region by using the contrast image and further considering the concentration gradient of the contrast agent.

  Next, details of the structural fluid analysis processing according to the present embodiment will be described. FIG. 7 is a diagram showing a typical flow of structural fluid analysis processing performed under the control of the system control unit 21 according to the present embodiment. 8 and 9 show the flow of processing from the data type. FIG. 9 differs in that it uses structural fluid analysis for the flow of FIG. 8, and is otherwise the same. FIG. 10 specifically shows the image processing device 27.

  As shown in FIG. 7, in the structural fluid analysis process, first, the medical image file to be processed is read from the storage device 33 by the 4D data acquisition unit 41 under the control of the system control unit 21. The medical image file includes, in addition to time-series CT image data relating to the subject into which the contrast agent has been injected, pressure value data relating to the blood vessel lumen obtained by the blood pressure data obtaining unit 41, flow rate / flow velocity. Data on the observed value of the blood flow index acquired by the data acquisition unit 43 and the like, and a plaque index are included. Other than the CT image, an MRI image or an ultrasonic echo image may be used. The time-series CT image data 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.

  The system control unit 21 causes the image processing device 27 to perform area setting processing (step S1). In step S <b> 1, the image processing 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 analysis target region and the identification target region are set in the blood vessel region by an instruction from the user via the input device 29 or by image processing.

  The structure of the blood vessel will be briefly described. 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. 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 image processing device 27 to perform image processing on the time-series CT image to obtain a time-series blood vessel shape index, a time-series blood vessel shape deformation index, and a CT value change index. calculate. (Step S2). More specifically, the time-series blood vessel shape deformation index is calculated by performing image analysis processing on the time-series CT image by the image analysis / image tracking unit 44 to obtain a time-series blood vessel shape index. It is calculated by performing a tracking process.

  More specifically, in the analysis processing, a blood vessel region is extracted from each CT image, and a pixel region related to the lumen of the blood vessel (hereinafter referred to as a blood vessel lumen region) and a pixel region related to the blood vessel wall (hereinafter referred to as a blood vessel wall region). ). As a blood vessel morphology index, a three-dimensional coordinate of 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 is 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, it may be a vascular 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.

  In the tracking process, a plurality of tracking points such as a feature point, a feature shape, a representative point, and a pixel in a blood vessel region, blood, contrast medium, and proton are set by an instruction or image processing from the user via the input device 29. For example, a tracking point set such as a blood vessel bifurcation or a surface feature shape is set. From the displacement data of the tracking point set obtained by the tracking process at each time (each cardiac phase), the temporal change of the displacement of the node on the vascular wall surface or inside the vascular wall or in the vascular lumen of the dynamic model is calculated by interpolation processing etc. And given as forced displacement. For example, a node on the blood vessel core line is defined in the dynamic model. From the temporal change in the displacement of the node on the surface of the blood vessel wall, inside the blood vessel wall or inside the blood vessel in the mechanical model, the deformation in the blood vessel core line direction is extracted, and deformations related to torsion and bending are extracted and the nodes in the cross section perpendicular to the blood vessel core line and the core line are extracted. It may be expressed by giving it as a forced displacement. 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. Further, image analysis / tracking processing will be described. For example, it is assumed that a time-series medical image includes 20 CT images per heartbeat. That is, it is assumed that 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. The form of the core wire changes as the cardiac phase progresses.

  Nodes P1 to P10 in the dynamic model are set on the blood vessel core line and are mechanically 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 point 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. By extracting forced displacement such as expansion / contraction, torsion and bending in the blood vessel core direction between the nodes P13 and P14 from the image analysis / tracking processing unit 53, the forced displacement of the node P13 (moving 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. 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.

  Based on the coordinates of the tracking point and the movement vector, a forcible displacement (movement displacement in the three-dimensional space and rotational displacement in the direction of the core line) of each node on the core line is calculated, and a blood vessel shape deformation index is calculated. For example, the time change of the coordinate difference between two adjacent nodes is calculated as the expansion / contraction distance ΔL in the core line direction. In addition, with respect to the nodes on each core line, the time change of the distance between the node and other nodes on the cross section of the blood vessel region including the node (nodes in the blood vessel lumen, the blood vessel wall, or the plaque region) is changed by the expansion / contraction distance in the radial direction. Calculated as Δr. Further, for each tracking point, the twist angle Δθ in the core line direction of the node on the core line is calculated based on the coordinates of the plurality of tracking points near the tracking point and the movement vector. Alternatively, the blood flow contrast index or proton image tracking may be used to calculate the flow velocity or the average flow velocity or average flow rate in the cross section in the core line direction as the blood flow index.

  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 a shape history, and the time-series blood vessel shape deformation index is referred to as a forced displacement history.

  When step S <b> 2 is performed, the system control unit 21 causes the image processing device 27 to surround the stenosis site included in the analysis target region based on the time-series blood vessel shape index, the time-series blood vessel shape deformation index, and the CT value change index. The CT value change index and the blood vessel cross-sectional shape deformation index due to the change in the contrast agent flow rate in the local blood vessel region are calculated (step S3).

  Next, the system control unit 21 causes the image processing device 27 to perform a construction process (step S4). In step S4, the dynamic model construction unit 55 of the image processing apparatus 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 image). Then, a dynamic model relating to the analysis target region is provisionally constructed on the basis of DICOM data such as an ultrasonic echo image. The dynamic model is a numerical model related to an analysis target region for performing structural fluid analysis.

  Step S4 will be described in detail. In building a dynamic model, first, a shape model for solving a dynamic model (mathematical model) is built based on a medical image and a shape history. The shape model schematically represents the geometric structure of the blood vessel region at each time. The shape model is divided into, for example, a plurality of discretized regions. The vertices of each discretized region are called nodes. A shape model for each time may be constructed based on the blood vessel region and the blood vessel shape index included in the medical image for each time, or based on the blood vessel region and the blood vessel shape index included in the medical image at a specific time phase. Thus, a shape model for each time may be constructed. Further, 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 is most contracted is set as the time phase in the no stress state. Assume no stress.

  The shape model has a blood vessel lumen region and a blood vessel wall region outward from the core line. 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.

  As shown in FIG. 11, when a shape model is constructed, sampling values relating to parameters of latent variables obtained from the probability distribution and variable range of each latent variable (for example, from a set of combinations of parameters by Markov chain Monte Carlo method). Sampling) in the dynamic model. For example, the analysis condition identification unit 45 sets a region to be identified for boundary conditions related to the entrance (hereinafter referred to as boundary condition identification region) at the end of the aortic region R1 on the aortic origin side, and the end of the right coronary artery region R2 and the left coronary artery region A boundary condition identification region relating to the exit is set at the end of R3. A sampling value related to a boundary condition parameter obtained from a boundary condition probability distribution or a variable range is assigned to each boundary condition identification region. In addition, the aorta region R1, the right coronary artery region R2, and the left coronary artery region R3 include 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 load condition identification region). Set). Sampling values related to the material model parameters obtained from the probability distribution and variable range of the material model are assigned to each material model identification region, and sampling related to the load condition parameters obtained from the probability distribution of the load condition and variable range is assigned to each load condition identification region. Assign a value. It is said that blood vessels have residual stress even when the flow rate is zero. For example, in the dynamic model construction process S4, the residual stress when the flow rate is 0 may be assigned to the analysis target region as the initial value of the load condition. Further, in the dynamic model construction process S4, a region for which the geometric structure is to be identified (hereinafter referred to as a geometric structure identification region) may be set 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 and the boundary threshold value of the living tissue Or the like. Although details will be described later, a material model may be set in the plaque region. Details of the material model will be described later.

  When the shape model is constructed, in the mechanical model construction processing S4 in the blood vessel stenosis model construction unit 46, the time-series blood vessel shape deformation index calculated in step S2, that is, the forced displacement history is assigned to the shape model. A shape model to which a latent variable and a forced displacement history are assigned is called a dynamic model.

  The image processing apparatus 27 according to the present embodiment performs reverse analysis using the dynamic model provisionally constructed in step S4, and statistically identifies latent variables set in the dynamic model. The statistical identification process is performed in step S7 described later. Steps S5 and S6 are provided for calculating a blood vessel shape index and a blood flow index used for statistical identification processing, respectively.

  When step 4 is performed, the system control unit 21 causes the vascular stress analysis unit 47 of the image processing device 27 to perform vascular stress analysis processing (step S5). In step S5, the vascular stress analysis function of the image processing device 27 performs a 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 the lumen region or a cross-sectional shape index of the 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 target pixel of the lumen region and the geometric structure parameters of the lumen region (such as the radius of the lumen region and the diameter of the lumen region). 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 parameter 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 4 is performed, the system control unit 21 causes the vascular fluid analysis unit 48 of the image processing device 27 to perform blood fluid analysis processing (step S6). In step S <b> 6, the blood fluid analysis function of the image processing 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 S5 and S6 are performed, the system control unit 21 causes the image processing device 27 to perform identification processing (step S7). In step S7, the statistical identification function of the image processing device 27 is a blood vessel shape index in which the predicted value of the blood vessel shape index calculated in step S5 and the predicted value of the blood flow index calculated in step S6 are collected in advance. 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.

  The parameters of the latent variable are statistically identified so that the predicted value of the blood vessel shape index matches the observed value of the blood vessel shape index. The parameters of the latent variable are statistically identified so that the predicted value of the blood flow index matches the observed value of the blood flow index.

  Specifically, in step S7, a data distribution based on the predicted value and the observed value of the blood vessel shape index calculated in step S5 is set. 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. Specifically, a normal distribution function value related to 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, a prior distribution (prior probability distribution) is assigned to the latent variable of the dynamic model. Specifically, a prior distribution is assigned to each of the parameters related to the uncertainty of the material model, boundary conditions, load conditions, and time-series form and shape deformation indices. 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. By executing a Monte Carlo simulation related to the internal pressure value within the assumed range, a probability distribution of internal pressure values, that is, a prior distribution is calculated for each discretized region. In addition, as a prior distribution, if the pressure distribution in the core line direction is smooth, the pressure change over time is smooth, and there is no backflow of blood flow, the average in the core line direction For example, a probability distribution expressed mathematically by a multivariate normal distribution function may be set as a 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, for each latent variable, a posterior distribution (a posteriori probability distribution) is calculated by performing statistical identification processing on the prior distribution and the data distribution. Examples of the statistical identification process include a hierarchical Bayes model and a Markov chain model. And about each latent variable, the parameter of each latent variable is identified from statistical values, such as the mode value and average value of posterior distribution. 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. 12 is a diagram for explaining the calculation of the posterior distribution and the identification of the average internal pressure regarding 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. 12, 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.

  Further, the statistical identification function first sets a data distribution based on the predicted value and the observed value of the blood flow index calculated in step S6. Next, a prior distribution is assigned to the latent variable 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. Probability distribution of material model parameters for each discretized region, i.e., prior distribution can be set, limited to these assumed ranges, and according to the assumed probability distribution, Monte Carlo simulation on the material model parameters can be executed, Sampling values of material model parameters (latent variables) for setting to a dynamic model can be obtained. Next, for each latent variable, a posterior distribution is calculated by performing statistical identification processing on the prior distribution and the data distribution, and parameters of each latent variable are identified from the calculated statistical value of the posterior distribution. 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. 13 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 Bayesian model and a Markov chain Monte Carlo method. As shown in FIG. 13, it is assumed that the blood vessel model is the same as that in FIG. By limiting to the material model identification region, the posterior distribution of the parameter (for example, equivalent elastic modulus) of the material model of the blood vessel wall is calculated, 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 the 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, image tracking of protons 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, 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 may be set as the prior distribution. 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.

  When step S7 is performed, the system control unit 21 causes the image processing device 27 to perform setting processing (step S8). In step S8, the parameter of the latent variable calculated in step S7 is set in the dynamic model.

  When step S8 is performed, the system control unit 21 determines whether the identification end condition is satisfied (step S9). When it is determined in step S9 that the identification end condition is not satisfied (step S9: NO), the system control unit 21 repeats steps S5, S6, S7, S8, and S9. 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. If the identification termination condition is satisfied, the latest dynamic model at that time is set as the final dynamic model.

  When the final mechanical model is constructed, the mechanical model construction function is a model (hereinafter referred to as a related model) that 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. Is called). The related model is stored in the storage device 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 S5 to S9 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 using internal pressure and external pressure using the equation of material dynamics of a thick-walled cylinder, the Hagen-Poiseuille flow and the 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 S9 that the identification completion receipt is satisfied (step S9: YES), the system control unit 21 may cause the image analysis / tracking processing function to perform a correction process (step S10). In step S10, the image analysis / tracking processing function uses the structural fluid analysis result (predicted value of mechanical index and predicted value of 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 device 31 displays a diagnosis result based on the corrected time-series medical image. Thereby, the blood vessel analysis function can display the diagnosis result in consideration of the final dynamic model. Alternatively, the display device 31 may display on the screen a blood vessel location / region where the identification by the inverse analysis and the observation result do not match by the structural fluid analysis. 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 S10 is performed, the system control unit 21 causes the image processing device 27 to perform vascular stress analysis processing and also to perform vascular stress analysis processing. In the vascular stress analysis process, the final mechanical model is subjected to vascular stress analysis 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. In the blood fluid analysis, the temporal distribution of the predicted value of the blood flow index in time series is calculated by performing the blood fluid analysis on the tentatively constructed dynamic model. 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.

  In step S11, the display device 31 displays the predicted value of the time-series dynamic index and the predicted value of the time-series blood flow index. In step S12, the display device 31 displays a time-series stenosis index such as a pressure change or flow rate change before and after the stenosis, and further a contrast agent concentration change. For example, the display device 31 displays a time-series dynamic index or a time-series blood flow rate index, and further a stenosis index in a moving image of a time-series dynamic model in a color corresponding to the value. Therefore, the display device 31 holds a color table indicating the relationship between each value and a color value (for example, RGB). The display device 31 specifies a color value corresponding to the value using a color table, and displays a discretization region corresponding to the predicted value with a color corresponding to the specified color value.

  As described above, due to the change in the contrast agent flow rate in the local vascular region around the stenosis site included in the analysis target region based on the shape index, shape deformation index, and CT value change index over a plurality of time phases of the analysis target region The CT value change index to be calculated and the blood vessel cross-sectional shape deformation index in the local blood vessel region are calculated, and the stenosis index for the stenosis site is calculated with high accuracy based on the CT value change index and the blood vessel cross-sectional shape deformation index in the local blood vessel region. be able to.

  As described above, according to the present embodiment, based on the image analysis of blood vessels (including blood) and the structural fluid analysis, it is possible to analyze stenosis indices such as changes in blood flow volume and pressure loss due to stenosis with high accuracy. It can be done.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

  DESCRIPTION OF SYMBOLS 10 ... CT mount, 11 ... X-ray tube, 13 ... X-ray detector, 15 ... Data acquisition device, 20 ... Console, 21 ... System control part, 23 ... Mount control part, 25 ... Reconstruction device, 27 ... Image processing Device 29 ... Input device 31 ... Display device 33 ... Storage device.

Claims (8)

A storage unit for storing data of medical images over a plurality of time phases related to a blood vessel of a subject into which a contrast medium is injected;
A setting unit for setting a region to be analyzed in a blood vessel region included in the medical image over the plurality of time phases;
An image processing unit that performs image processing on the medical images over the plurality of time phases and calculates a shape index, a shape deformation index, and a CT value change index over the plurality of time phases of the analysis target region;
CT value change index resulting from a change in contrast agent flow rate in a local blood vessel region around the target region included in the analysis target region based on the shape index, shape deformation index, and CT value change index over the plurality of time phases And a calculation unit for calculating a blood vessel cross-sectional shape deformation index in the local blood vessel region,
A structural fluid analyzer that calculates a stenosis index for the target region based on a CT value change index and a blood vessel cross-sectional shape deformation index in the local blood vessel region;
An apparatus for analyzing blood vessels. A storage unit for storing data of medical images over a plurality of time phases related to a blood vessel of a subject into which a contrast medium is injected;
A setting unit that sets an analysis target region in a blood vessel region included in the medical image over the plurality of time phases, and sets a latent variable identification region in the analysis target region;
Image processing for calculating a CT value change resulting from a change in a flow rate of a contrast agent and a shape index, a shape deformation index, and a contrast agent over the plurality of time phases of the analysis target region by performing image processing on the medical images over the plurality of time phases And
Changes in the flow rate of the contrast agent over the plurality of time phases in the local blood vessel region around the target portion included in the analysis target region based on the shape index, the shape deformation index, and the CT value change index over the plurality of time phases A calculation unit for calculating a CT value change index caused by the blood vessel and a blood vessel cross-sectional shape deformation index in the local blood vessel region;
Temporarily construct a dynamic model for the analysis target region based on the shape index and shape deformation index of the analysis target region over the plurality of time phases, and the CT value change index and blood vessel cross-sectional shape deformation index in the local blood vessel region A construction department to
At least one of the predicted value of the blood vessel shape index and the predicted value of the blood flow index based on the tentatively constructed dynamic model matches at least one of the observed value of the blood vessel shape index and the observed value of the blood flow index. An identification unit for identifying a latent variable related to the latent variable identification region,
A stenosis index calculator that performs structural fluid analysis on the dynamic model in which the identified latent variable is assigned to the latent variable identification region to calculate a predicted value of the stenosis index for the target site;
An apparatus for analyzing blood vessels.   The calculation unit calculates a temporal change in CT value in a local blood vessel region upstream from the target site as the CT value change index, and the upstream local blood vessel region and the upstream as the blood vessel cross-sectional shape deformation index. The blood vessel analysis device according to claim 1 or 2, wherein the deformation of the cross-sectional shape of the blood vessel region in the local blood vessel region on the downstream side of the target site is calculated.   The calculation unit calculates, as the CT value change index, a spatial change in CT value between a local blood vessel region upstream of the target site and a local blood vessel region downstream of the target site, and the blood vessel cross section The blood vessel analysis device according to claim 1 or 2, wherein a change in cross-sectional shape of the blood vessel region between the upstream local blood vessel region and the downstream local blood vessel region is calculated as a shape deformation index.   Based on CT image analysis and image tracking of vessel expansion state and contraction state and intermediate state of blood vessel, blood vessel cross-sectional shape deformation, CT value change data, as the first relational model, blood vessel cross-sectional shape deformation, internal pressure, wall thickness and no A relational model including at least two parameters of the stress state cross-sectional shape and the vascular material constitutive equation, and at least two of pressure loss, flow velocity, cross-sectional shape, and blood material constitutive equation before and after vascular stenosis as a second relational model Using the relational model including the above parameters and the third relational model, a relational model including at least two or more parameters among the CT value, the cross-sectional shape, the flow velocity, the contrast agent concentration, and the contrast agent material constitutive formula is used. The amount of deformation of the blood vessel cross-section in the expanded state, the contracted state and the intermediate state, the amount of deformation of the blood vessel cross-sectional shape before and after stenosis, The inter-state CT value change amount and CT value change data before and after stenosis are used as input to identify the internal pressure in the blood vessel cross section before and after stenosis, or the flow rate or flow velocity or contrast agent concentration in the blood vessel cross section before and after stenosis. Characteristic blood vessel analyzer. Computer
Based on CT image analysis and image tracking of vessel expansion state and contraction state and intermediate state of blood vessel, blood vessel cross-sectional shape deformation, CT value change data, as the first relational model, blood vessel cross-sectional shape deformation, internal pressure, wall thickness and no A relational model including at least two parameters of the stress state cross-sectional shape and the vascular material constitutive equation, and at least two of pressure loss, flow velocity, cross-sectional shape, and blood material constitutive equation before and after vascular stenosis as a second relational model Using the relational model including the above parameters and the third relational model, a relational model including at least two or more parameters among the CT value, the cross-sectional shape, the flow velocity, the contrast agent concentration, and the contrast agent material constitutive formula is used. The amount of deformation of the blood vessel cross-section in the expanded state, the contracted state and the intermediate state, the amount of deformation of the blood vessel cross-sectional shape before and after stenosis, As an input to identify the internal pressure in the blood vessel cross section before and after the stenosis, or the flow rate or flow velocity or contrast agent concentration in the blood vessel cross section before and after the stenosis, using the CT value change amount of the interstitial state and the CT value change data before and after the stenosis as input Blood vessel analysis program to make it function. A storage unit for storing data of medical images over a plurality of time phases related to a blood vessel of a subject into which a contrast medium has been injected, and a system control unit for executing structural fluid analysis based on the medical images over the plurality of time phases An operation method of a blood vessel analysis device comprising:
The system controller is
Control the setting unit, set the analysis target region in the blood vessel region included in the medical image over the plurality of time phases,
Controlling the image processing unit, image processing medical images over the plurality of time phases to calculate the shape index, shape deformation index, and CT value change index over the plurality of time phases of the analysis target region,
The calculation unit is controlled to change the contrast agent flow rate in the local blood vessel region around the target region included in the analysis target region based on the shape index, the shape deformation index, and the CT value change index over the plurality of time phases. Calculate the resulting CT value change index and the blood vessel cross-sectional shape deformation index in the local blood vessel region,
Controlling the structural fluid analysis unit to calculate a stenosis index for the target region based on a CT value change index and a blood vessel cross-sectional shape deformation index in the local blood vessel region ;
Method for operating a blood tube analyzer. On the computer,
A setting function for setting a region to be analyzed in a blood vessel region included in a medical image over a plurality of time phases related to a blood vessel of a subject into which a contrast medium has been injected;
An image processing function that performs image processing of the medical images over the plurality of time phases to calculate a shape index, a shape deformation index, and a CT value change index over the plurality of time phases of the analysis target region;
CT value change index resulting from a change in contrast agent flow rate in a local blood vessel region around the target region included in the analysis target region based on the shape index, shape deformation index, and CT value change index over the plurality of time phases And a calculation function for calculating a blood vessel cross-sectional shape deformation index in the local blood vessel region,
A structural fluid analysis function for calculating a stenosis index for the target region based on a CT value change index and a blood vessel cross-sectional shape deformation index in the local blood vessel region;
Blood vessel analysis program that realizes.