WO2017047821A1

WO2017047821A1 - Method and device for visualizing tissue blood vessel characteristics

Info

Publication number: WO2017047821A1
Application number: PCT/JP2016/077756
Authority: WO
Inventors: 高伸八木
Original assignee: イービーエム株式会社
Priority date: 2015-09-18
Filing date: 2016-09-20
Publication date: 2017-03-23
Also published as: JPWO2017047821A1

Abstract

[Solution] This method is for visualizing tissue blood vessel characteristics and comprises: a step in which a computer processes a medical image containing an area to be analyzed, identifies a blood vessel responsible for supplying blood to the area to be analyzed, and outputs the blood flow rate thereof; and a step in which a computer calculates the tissue blood vessel characteristics of an area being analyzed, from the blood flow rate, and displays these characteristics superimposed upon or separately from the medical image.

Description

Method and apparatus for visualizing tissue vascular properties

The present invention relates to a technique for calculating and displaying tissue blood vessel characteristics such as tissue blood volume and blood vessel peripheral resistance from blood vessel morphology imaging data without using functional imaging such as PET / SPECT.

Conventionally, non-invasive imaging used in the medical field can be classified into two types: (1) morphological imaging and (2) functional imaging. Among morphological imaging, those that image the shape of blood vessels by MRA, CTA, DSA, etc. are typical. Morphological imaging is limited to visualization of blood vessels on the order of 0.5 mm at most because of limited spatial resolution. On the other hand, blood supply in the deep part of the heart and brain relies on capillaries. Capillary scales are on the order of 0.001 to 0.01 mm, and morphological imaging is not possible. Therefore, functional imaging such as PET or SPECT is used. In PET and SPECT, a substance that permeates capillaries is injected into blood and its distribution is measured. In this case, the substance becomes a radioactive substance, and the tissue blood volume supplied to the tissue and the tissue blood volume held by the tissue can be measured using the property of permeating through the walls of the capillaries.

However, when trying to visualize the blood vessel characteristics of tissues including capillaries using non-invasive imaging, it is divided into morphological imaging and functional imaging. There was a problem of ending up.

Furthermore, it is necessary to introduce the concept of erasure resistance in order to connect morphological images and functional images. However, erasure resistance has been at a conceptual level so far. In other words, in order to achieve erasure resistance, the pressure and flow rate in the distal blood vessel are required, and the measurement of these values requires invasive measurement using a catheter, and there is a problem that it cannot be performed non-invasively. It was.

On the other hand, considering that the growth of cancer and tumors requires that the capillaries increase locally, the capillaries that supply the blood flow in the tissue cannot be imaged, but the blood vessels in the capillaries cannot be imaged. If the responsible blood vessels that supply the flow can be identified, it can be expected to create a new treatment.

From the above background, the inventors have come to the conclusion that the above-mentioned problems can be fundamentally solved if the data obtained by functional imaging can be mathematically calculated from the data obtained from morphological imaging such as medical images. For this purpose, the inventors have sincerely developed a method for performing functional imaging based on morphological imaging, and based on vascular morphological imaging such as MRA, CTA, DSA, and the like as tissue imaging such as PET / SPECT. A method for mathematically calculating tissue vascular characteristics such as

In order to solve the above problems, according to a first main aspect of the present invention,
A computer that processes a medical image including the analysis target region, identifies a responsible blood vessel that supplies blood to the analysis target region, and outputs the blood flow; and the computer calculates a tissue blood vessel in the analysis target region from the blood flow. There is provided a method for visualizing tissue vascular characteristics comprising calculating characteristics and superimposing or individually displaying on the medical image.

According to one embodiment of the present invention, the step of identifying the responsible blood vessel includes a step in which a computer extracts a nearest blood vessel with respect to the analysis target region, and a flow stream of a blood flow that flows through the nearest blood vessel. A calculating step, and a computer determining the responsible blood vessel based on the calculated streamline.

According to another embodiment, the tissue blood vessel characteristic is a tissue blood flow rate supplied to the analysis target area, and the step of calculating the tissue blood vessel characteristic includes a tissue blood flow rate stored in a memory by a computer. A step of reading a calculation coefficient, and a step of outputting a tissue blood flow rate per unit weight of the analysis target region by multiplying the blood flow rate of the responsible blood vessel by the computer blood flow calculation coefficient read out by the computer. Is.

According to still another embodiment of the present invention, the tissue vascular characteristic is an erasure resistance in a capillary blood vessel of the analysis target region, and the step of calculating the tissue vascular characteristic further includes: A step of calculating a pressure loss value of the responsible blood vessel based on a blood flow rate; and a step of calculating and outputting a peripheral resistance of the analysis target region based on the pressure loss value.

Further, it is preferable to use a blood vessel tomographic image by MRA, CTA, or DSA as the medical image.

According to yet another embodiment of the present invention, the blood flow rate is calculated from an equation modeled by a computer from Hagen Poiseuille theory and a constant shear stress adjustment function of endothelial cells.

According to another embodiment, the tissue blood flow rate calculation coefficient is obtained by statistically processing the relationship between the vascular blood flow rate obtained from morphological imaging and the tissue blood volume obtained from functional imaging, and is stored in the memory. It has been done.

According to yet another embodiment of the present invention, the step of calculating the pressure loss value is calculated by applying the flow rate to an equation modeled by a computer based on the Hagen Poiseuille theory.

According to still another embodiment, the method further includes a step of calculating a blood vessel shape of each blood vessel from the medical image.

According to yet another embodiment, the method further includes a step of physically calculating a blood flow in each blood vessel based on the calculated blood vessel shape.

According to still another embodiment of the present invention, the step of identifying the responsible blood vessel further includes a step of superimposing and displaying the medical image on a calculation result of blood flow analysis, and the superimposed medical image The user has a step of designating a target point of the analysis target region and a step of extracting a nearby blood vessel from a medical image designating the target point.

According to another embodiment, the step of calculating the streamline comprises calculating a streamline by placing a seed point on the extracted nearest blood vessel at a position closest to the target point. is there.

According to a second main aspect of the present invention, a computer processes a medical image including an analysis target region, identifies a responsible blood vessel that supplies blood to the analysis target region, and outputs the blood flow rate. And a tissue blood vessel characteristic calculating unit that calculates a tissue blood vessel characteristic of the analysis target region from the blood flow volume, and superimposes or individually displays the tissue blood vessel characteristic on the medical image. Provided.

According to a third main aspect of the present invention, a computer processes a medical image including an analysis target area, identifies a responsible blood vessel that supplies blood to the analysis target area, and outputs the blood flow volume; A computer software program for visualizing tissue blood vessel characteristics including a command for calculating a tissue blood vessel characteristic of the analysis target region from the blood flow volume and superimposing or individually displaying the tissue blood vessel characteristics on the medical image; Provided.

It should be noted that the features of the present invention not listed above are provided so as to be practiced by those skilled in the art in the embodiments and drawings described below.

FIG. 1 is a schematic configuration diagram showing an embodiment of the present invention. FIG. 2 is a functional configuration diagram in the present embodiment. 3A to 3E are diagrams for explaining the steps S1 to S4 in FIG. 4 (a) and 4 (b) are diagrams showing examples of blood vessel adhesion artifacts. FIGS. 5A to 5C are diagrams for explaining the graphing and blood vessel adhesion detection steps of the present embodiment. FIGS. 6A and 6B are views for explaining the blood vessel adhesion separation process of the present embodiment. FIG. 7 is a diagram illustrating processing of the shape measurement unit of the present embodiment. FIG. 8 is a table of parameters measured by the shape measuring unit of the present embodiment. FIG. 9 is an explanatory diagram when the blood vessel characteristic calculation unit according to the embodiment of the present invention calculates the tissue blood volume. FIG. 10 is an example in which the calculation result of the blood vessel characteristic calculation unit of the present embodiment is output by being superimposed on a medical image. FIG. 11 is a graph of blood vessels related to the pressure calculation unit according to the embodiment of the present invention. FIG. 12 is an example of a pressure loss value calculated by the pressure calculation unit. FIG. 13 is a schematic diagram for explaining the erasure resistance calculated by the erasure resistance calculation unit of the present embodiment. FIG. 14 is a functional configuration diagram in one embodiment of the present invention. FIG. 15 is an explanatory diagram of the superimposing unit. FIG. 16 is an example of a screen display of the target designation unit of the present embodiment. FIG. 17 is a diagram for explaining the nearest blood vessel extraction processing by the neighboring blood vessel extraction unit of the present embodiment. FIG. 18 is a diagram for explaining processing of the streamline calculation unit of the present embodiment.

Hereinafter, preferred embodiments of the present invention will be described in detail.

FIG. 1 is a schematic configuration diagram showing a blood vessel characteristic display device according to this embodiment.

FIG. 1 shows a system configuration diagram of a blood vessel characteristic display device 100 according to an embodiment of the present invention. The blood vessel characteristic display device 100 includes a control unit 1, an operation unit 2, a recording unit 3, a display unit 4, and the like. The control unit 1 includes a CPU and a memory (both not shown), medical image data recorded in the recording unit 3, computer software, and various variables and coefficients (a shear stress value, tissue blood, which will be described later). 2) are appropriately read out and developed on the memory, thereby functioning as each component as shown in FIG. That is, it functions as a medical image input unit 21, a thinning unit 22, a graphing unit 23, a shape measuring unit 24, a vascular blood flow rate calculating unit 25, a responsible blood vessel identifying unit 26 (not shown), and a blood vessel specific calculating unit 27. .

Next, with reference to FIGS. 2 to 10, the operations executed by each component will be described in detail step by step.

(Medical image input unit: Step S1)
The medical image input unit 21 functions as an input interface for inputting medical images and the like. The input medical image is preferably a tomographic image captured by an imaging device such as MRA (magnetic resonance angiography), CTA (computer tomographic angiography), DSA (digital differential angiography), and the medical image is a network (not shown). The data is once recorded in the recording unit 3 by other transfer means and then input to the control unit 1 (FIG. 1).

In addition to the image (medical image), the medical image input unit 21 can also input parameters necessary for fluid analysis such as fluid physical properties, boundary conditions, and calculation conditions.

(Thinning section: Step S2)
The thinning unit 22 binarizes the medical image input by the medical image input unit 21 (step S2-1) and thins the line (step S2-2), and acquires the center line of the blood vessel. The processing in the thinning unit 22 will be described with reference to FIG.

Binarization (Step S2-1)
First, the thinning unit 22 binarizes the medical image ((FIG. 3A)) input in step S1, and extracts the three-dimensional shape of the blood vessel (FIG. 3B: step S2-1). In the embodiment, the thinning unit 22 automatically sets the binarization threshold so as to extract a characteristic specific to the blood vessel wall based on a histogram of luminance values of the entire image. The threshold value may be selected and set.

Thinning (Step S2-2)
Next, the thinning unit 22 performs thinning processing on the binarized three-dimensional blood vessel shape data to obtain a blood vessel center line (FIG. 3C: step S2-2). A plurality of algorithms are known for thinning and are not limited to a specific algorithm, but in this embodiment, the thinning unit centers the extracted voxel of the blood vessel region on the outer peripheral side (that is, the surface) of the blood vessel region. The core of the blood vessel is extracted by shaving and the center line is obtained by fitting a spline curve or the like in the blood vessel running direction.

(Graphing unit 23: Step S3)
Graphing (Step S3-1)
Next, the graphing unit 23 performs graphing using the blood vessel center line obtained by the thinning unit 22 (FIG. 3D: step S3-1). In this embodiment, the graphing is to label each blood vessel portion of the center line data, and the graphed data is referred to as graph data. In the graphing process, the graphing unit first divides the blood vessel center line into elements for each region. In this element division, as shown in FIG. 3 (d), the end / branch point (A, B, C,...) Is specified in the center line acquired by the thinning process, and the end / branch. This is done by dividing the center line by points. Hereinafter, each blood vessel portion corresponding to the center line between the divided end points and branch points is referred to as a blood vessel element. Thereafter, the graphing unit performs labeling (# 1, 2, 3,...) On the center line data of each blood vessel element to generate graph data (step S3-1).

In graphing, a loop shape as an artifact may occur in the blood vessel due to the adhesion of the blood vessel.

The graphing unit may include a process of determining the presence or absence of a blood vessel loop and separating the adhesion between blood vessels when a blood vessel loop is detected. As shown in FIGS. 4 (a) to 4 (b), the term “adhesion” used here appears to be adhered due to lack of image accuracy even though blood vessels are not originally adhered to each other. This is a phenomenon that occurs. This phenomenon is hereinafter referred to as “adhesion” or “adhesion artifact”.

Detection of adhesions (step S3-2)
Normally, a blood vessel is a loop circuit of a closed circuit at the whole body level, but a loop is not formed in a relatively microscopic region handled by blood flow analysis. Therefore, in this embodiment, the presence or absence of adhesion is detected according to the presence or absence of a loop. Specifically, as shown in FIGS. 5A to 5C, the adhesion site is detected by graphing the blood vessel shape and depth-first search of the graph (step S3-2). That is, end points and branch points are detected and their connection relations are examined.

For example, in the graph shown in FIG. 5B processed based on the blood vessel shape shown in FIG. 5A, the branch structure of the blood vessel is expressed. In this figure, what is indicated by a circle is a node, which indicates an end point or a branch point. When adhesion occurs, this graph appears as a closed circuit (loop), and this closed circuit is detected by searching the depth of the graph (FIG. 5C). This is an operation that follows the edges from the initial node through all nodes. The rules for tracing are as follows.
(1) When a branch point is reached, an edge that has not passed is selected and the process proceeds to the next node.
(2) When the end point is reached, return to the previous branch point.
(3) If there is no side that has not passed when returning to the branch point, the process returns to the previous branch point.

In FIG. 5 (c), the number of each node represents the visiting order of branch depth priority search. A solid arrow indicates a forward path, and a dotted arrow indicates a return path. In the example shown in FIG. 5C, a closed circuit is generated between the fifth and sixth. When the depth-first search is performed, it proceeds from the 5th node to 6th, 7th, 8th and returns to 6th. Here, since the outward path has passed through the lower side of the figure, the upper side has not yet passed. However, if you select the upper side, you will arrive at the number 5 that you have already visited. If there is no closed path, only the return path will return to the visited node, but if there is a closed path, the visited node will be reached in the forward path. Therefore, it is possible to detect a closed path by recording the passing situation of the nodes and sides, and the node at the time point determined to be closed (No. 6 in the example shown in FIG. 5C) is regarded as an adhesion site. Can do.

Separation of adhesion site (step S3-3)
When the graphing unit 23 detects the adhesion site, it next separates the adhesion site. This adhesion separation is performed by setting a certain threshold value and evaluating the degree of adhesion. If the degree of adhesion is equal to or greater than the threshold, the process returns to the starting point of the region division, and the degree of adhesion is positively reduced by binarization based on a stricter standard. If the degree of adhesion is less than or equal to the threshold, the adhesion site is separated based on the running direction and shape of the blood vessel connected to the adhesion site. The separation process is performed based on the blood vessel region, the center line, the traveling direction of the blood vessel, and the cross-sectional area. The blood vessel region, the center line, and the running direction of the blood vessel are obtained by region division and structural analysis, and the cross section of the blood vessel is obtained by measuring a cross section perpendicular to the blood vessel running direction of the blood vessel region.

The flow of separation processing is as follows.
(1) The adhesion section is obtained from the change in the cross-sectional area of the blood vessel.
(2) Estimate the center line of the adhesion section using the center line of the blood vessels before and after the adhesion section.
(3) The shape of each blood vessel is estimated by fitting two tangent ellipses in the blood vessel cross section of the adhesion section to the contour of the blood vessel surface.
(4) Divide the cross section of the adhesion section into two.

Hereinafter, with reference to FIG. 6, each of the separation processes (1) to (4) will be described in more detail. First, the cross-sectional area of the blood vessel before and after the adhesion section is measured. Since the two blood vessels are integrated in the part where the adhesion is made, when the cross-sectional area is plotted along the running of the blood vessel, only the adhesion section as shown in the lower part of FIG. The cross-sectional area increases. This change is detected and the adhesion interval is determined.

Next, paying attention to the center line of the blood vessel, the two center lines originally shifted to the inside, and finally touched to become a branch point. The one indicated by the alternate long and short dash line in the upper diagram of FIG. However, since it is originally two blood vessels, it should be two curves that do not intersect as shown by the dotted line in the upper diagram of FIG. Therefore, the original center line of the adhesion section is estimated by interpolating from the front and back center lines. Next, the outline of each blood vessel is estimated on the cross section of the adhesion section using the estimated center line. FIG. 6B shows a cross section perpendicular to the blood vessel running direction in the adhesion section. Two black dots 71 indicate the estimated center line. Assuming that the cross section of the blood vessel is an ellipse, the two ellipses 72 are fitted to the blood vessel contour 73 of the adhesion section under the condition that the centers of the two ellipses 72 are known and are in contact. Two ellipses fitted by dotted lines in FIG. 6B are shown. Finally, the inside of the adhesion section is divided into two regions according to the ratio of the diameters of the two blood vessels, and the separation process is completed.

(Shape Measurement Unit 24: Step S4)
The shape measuring unit 24 measures the shape of each blood vessel element obtained by the graphing process. Specifically, in this embodiment, the following parameters are measured (FIG. 8).
(1) Diameter (2) Concavity and convexity (3) Bending (4) Twisting (5) Branching angle (1) The diameter is an equivalent diameter of the blood vessel cross-sectional area in the blood vessel centerline running direction. (2) As shown in FIG. 7, the unevenness is quantified by three-dimensional information obtained by mapping each point of the line segment of the blood vessel surface running coordinate system and the height of the blood vessel that can be defined by a center line orthogonal section including the point. . (3) Bending and (4) Twist are quantified by approximating the blood vessel center line with a spline function. (5) The branching angle is an angle formed by the parent blood vessel and the daughter blood vessel at the blood vessel branching portion.

(Vessel blood flow rate calculation unit 25: Step S5)
The vascular blood flow rate calculation unit 25 calculates the blood flow rate in each vascular element using the following equation 1 based on the average blood vessel diameter output from the shape measurement unit 24.

Here, τ is wall shear stress and μ is blood viscosity. In this embodiment, τ and μ are given as constants, but values actually measured in the target patient may be used. Alternatively, the shear stress may be determined from an optimum shear stress diagram prepared separately according to aging or disease. Here, α is a coefficient for correcting the influence of the bending or meandering of the blood vessel.

(Responsible Blood Vessel Identification Unit 26)
The responsible blood vessel identification unit 26 not shown in FIG. 2 identifies the responsible blood vessel that supplies blood to the analysis target region of the blood vessel characteristics. In this process, after step S5, the three-dimensional shape data of the blood vessel acquired by the shape measuring unit 24 (step S4) is displayed on the screen so that the responsible blood vessel is extracted after the user designates the target. Alternatively, it may be performed in step S7B-1 instead of after step S5.

In yet another embodiment, the responsible blood vessel is identified after inputting the medical image in step S1. In this case, after step S1, a medical image is displayed on the screen to allow the user to specify a target, and the extraction of the nearby blood vessel, the identification of the nearest blood vessel, and the streamline calculation of the blood flow flowing in the nearest blood vessel are performed. Thus, the blood transport route for supplying blood to the target is visualized, and the responsible blood vessel is identified. This responsible blood vessel identification step will be described in detail with reference to FIGS. 14 to 18 in the description of another embodiment to be described later.

(Vessel characteristic calculation unit 27: Step S7)
The blood vessel characteristic calculation unit 27 calculates the blood vessel characteristic for each tissue or for each designated region using the responsible blood vessel identified by the responsible blood vessel identification unit 26. Details will be described for each tissue blood vessel characteristic calculated below.

(Tissue vascular characteristic A: visualization of tissue blood volume)
Tissue blood volume calculation unit 41 (step S7A-1)
Based on the vascular blood flow calculated by the vascular blood flow calculation unit 25, the tissue blood volume calculation unit 41 calculates the tissue blood flow using the following formula 2.

Here, Q _tissue is the tissue blood volume per unit weight [ml / min / gram], β is a proportionality coefficient, and Q _i is the blood flow of blood flowing through the nearest blood vessel extracted by the responsible blood vessel identification unit 26. In FIG. 9, Q _i = 150 is shown.

The proportionality coefficient β is a value obtained by statistically processing the relationship between the vascular blood flow and the tissue blood flow. More specifically, in this embodiment, for example, when targeting the brain, the blood flow supplied to the entire brain, that is, the integrated value of the vascular blood flow, and the blood flow supplied to the brain tissue are the same. When Q _tissue , which is a tissue blood flow per brain tissue unit weight, is integrated over the entire brain, the integrated value of the vascular blood flow is the same. β is a value calculated using such a law of conservation of blood flow mass. More specifically, β is acquired using mass conservation as a constraint condition so that Q _tissue can be acquired in association with the blood flow volume of the nearest blood vessel.

When the Q _tissue is a _tissue shown by a square in FIG. 9, the nearest blood vessel is # 1 shown in FIG. 9, and the tissue blood flow is calculated based on the blood flow flowing through this blood vessel. β is a value obtained by statistically processing the relationship between vascular blood flow and tissue blood flow. In this embodiment, vascular blood flow was obtained from morphological imaging such as phase contrast MRI, and tissue blood volume was obtained from functional imaging such as PET / SPECT.

Display of tissue blood volume Finally, the blood vessel characteristic visualization device 100 displays the tissue blood flow on the screen. In this embodiment, as shown in FIG. 10, a cross-sectional display of a medical image obtained by CT imaging with the result of the tissue blood flow rate calculation unit superimposed thereon is displayed. The result of the tissue blood flow rate calculation unit is the result of calculating the tissue blood volume in the brain three-dimensionally, that is, for each unit tissue. In this figure, the superimposed result is shown in a two-dimensional cross section, but the apparatus of the present invention may display the result in three dimensions. Here, the value of the tissue blood volume is displayed in four stages. For example, the magnitude of tissue blood flow is represented by red, yellow, green, and blue as shown in FIG. Assume that the red region has the highest tissue blood volume, and the tissue blood flow decreases in the order of yellow, green, and blue.

In the present embodiment, the case where the tissue blood vessel characteristic that is finally calculated and visualized is the tissue blood volume has been described, but an embodiment in which the tissue blood vessel characteristic that is calculated next is the erasure resistance will be described.

(Tissue vascular characteristic B: Visualization of erasure resistance)
Next, an embodiment in which the tissue blood vessel characteristic output from the blood vessel characteristic calculation unit 27 is the erasure resistance will be described. In this embodiment, the blood vessel characteristic calculation unit 27 further includes a pressure calculation unit 45 (step S7B-1) and an erasure resistance calculation unit 46 (step S7B-2).

Pressure calculation unit 45 (step S7B-1)
First, the pressure calculation unit 45 calculates the pressure loss value Δp in each vascular element using the following Equation 3 based on the data calculated by the shape calculation unit 24 and the blood flow rate calculation unit 25.

Here, μ is blood viscosity, l is blood vessel length, D is blood vessel diameter, and Q is blood flow. Β is a coefficient that corrects bending and meandering of blood vessels.

Specifically, first, the pressure calculation unit 45 extracts a basal blood vessel serving as a route of a main artery that supplies blood to each organ. In the example of the blood vessel (graph) shown in FIG. 11, the basal blood vessel corresponds to # 1. The algorithm for extracting the basal blood vessel is shape information or anatomical information, and there is no special designation. After that, the pressure calculation unit 45 calculates the pressure loss value of each blood vessel element (# 1, # 2, # 3,...) Using the above equation 3 based on the basal blood vessel as shown in FIG. Output.

Erasing resistance calculator 46 (step S7B-2)
Next, the erasure resistance calculation unit 46 calculates and outputs the erasure resistance value R_i using the following equation 4 based on the pressure loss value calculated by the pressure calculation unit 45.

Here, the arterial pressure P_i is calculated from the basal artery pressure Pref_artery and the aforementioned pressure loss value Δp. In the calculation of the erasure resistance value, an actual measurement value may be used for the venous pressure P_ (ref_vein). Alternatively, since the venous pressure P_ (ref_vein) is small relative to the arterial pressure, the erasure resistance value may be calculated by the following equation 5 ignoring this.

(Visualization of blood flow transport path: identification of responsible blood vessels)
Next, an embodiment in the case of visualizing a blood flow transport route for a target tissue will be described with reference to FIGS. The present embodiment relates to a method and an apparatus for visualizing a blood transport route using a main artery that can be imaged as a form. The method is by computer simulation. Perform blood simulation from morphology. After that, a streamline is created with a seeding point near the target (for example, a reservoir or a tumor in the brain). In this case, the streamline should be created upstream of the flow method. The responsible blood vessel is identified by following the trajectory of this streamline.

FIG. 15 is a diagram showing a functional configuration of the blood flow transport path visualization device in the present embodiment.

Input unit (step SC1)
This apparatus receives medical image data and velocity field / pressure field data acquired by performing blood flow analysis by computational fluid dynamics (CFD) using the image data (step SC1). ). The medical image data is a group of tomographic images captured by an imaging apparatus such as MRA, CTA, and DSA, similarly to the medical image input in the above-described embodiment.

Superposition unit 32 (step SC2)
As shown in FIG. 15, the superimposing unit 32 receives the data of the coordinate system (Ximage, Yimage, Zimage) of the input medical image data and the coordinate system of the velocity field / pressure field (XCFD, YCFD, ZCFD), and is the same. In order to express in the coordinate system, the coordinate system of the medical image data is transformed into the coordinate system of the velocity field / pressure field.

Target designation unit 33 (step SC3)
Next, the target designating unit 33 displays the medical image data coordinate-converted by the superimposing unit 32 on the display unit, and allows the user to select a target vascular lesion site in the image data via the user interface. Examples of targets are cerebral aneurysms and brain tumors. FIG. 16 shows an example of a medical image displayed on the display unit. In FIG. 16, what is indicated by a double circle corresponds to a portion designated as a target.

Neighboring blood vessel extraction unit 34 (step SC4)
Next, processing performed by the nearby blood vessel extraction unit 34 will be described with reference to FIGS. 17 and 18.
(Step SC4-1)
The nearby blood vessel extraction unit 34 first performs the binarization / thinning processing of steps S2-1 to S2-2 on the medical image specified by the target specification unit 33, and then steps S3-1 to S3. -3 Perform graphing / adhesion detection / separation processing.
(Step SC4-2)
Next, the nearby blood vessel extraction unit 34 extracts a nearby blood vessel for the target designated by the target designation unit 33. The neighboring blood vessel extraction unit classifies the neighboring blood vessels according to the distance from the target, but considers a continuous blood vessel as one. The distance from the target is calculated using a technique such as Voronoi diagram. FIG. 17 shows a stage where the distance from the target to each blood vessel is measured and the nearest blood vessel is identified. In FIG. 17, a target indicated by a double circle is the target, and a section indicated by a dotted line is the nearest blood vessel identified.

Streamline calculation unit 35 (step SC5)
The streamline calculation unit 35 calculates and visualizes the blood transport route to the target. Specifically, a seed point is placed in a nearby blood vessel extracted by the nearby blood vessel extraction unit 34, and a blood flow path to the target is calculated by calculating a streamline from the seed point in the nearby blood vessel toward the upstream in the flow direction. Is visualized.

(Stream line display 36: Step SC6)
FIG. 18 shows an example of a result of calculating a blood flow transport route by arranging a seed point at a position closest to the target in a nearby blood vessel. By visualizing the blood flow transportation path with respect to the target by the streamline calculation unit 35, it becomes possible to identify the responsible blood vessel supplying blood to the target. This method for identifying the responsible blood vessel by visualizing the blood flow transport route is also used by the responsible blood vessel identifying unit 26 in the above-described embodiment.

In addition, it goes without saying that the present invention can be variously modified, and is not limited to the above-described embodiment, and can be variously modified without changing the gist of the invention.

Claims

A computer processing a medical image including an analysis target region, identifying a responsible blood vessel supplying blood to the analysis target region, and outputting the blood flow;
A method for visualizing the tissue blood vessel characteristic comprising: calculating a tissue blood vessel characteristic of the analysis target region from the blood flow volume and superimposing or individually displaying the tissue blood vessel characteristic on the medical image.
The method of claim 1, wherein
Identifying the responsible blood vessel comprises:
A computer extracting a nearest blood vessel for the analysis target region;
A computer calculating a streamline of blood flow through the nearest blood vessel;
A computer further comprising: determining the responsible blood vessel based on the calculated streamline.
The method of claim 1, wherein
The tissue blood vessel characteristic is a tissue blood flow volume supplied to the analysis target region,
The step of calculating the tissue vascular characteristic comprises:
Reading a tissue blood flow calculation coefficient stored in a memory by a computer;
And outputting a tissue blood flow rate per unit weight of the analysis target region by multiplying the blood flow rate of the responsible blood vessel by the computer-calculated tissue blood flow rate calculation coefficient.
The method of claim 2, wherein
The tissue vascular characteristic is a erasure resistance in capillaries in the analysis target region,
The step of calculating the tissue vascular characteristic further includes:
Calculating a pressure loss value of the responsible blood vessel based on a blood flow rate of the responsible blood vessel;
A computer calculating and outputting an erasing resistance of the analysis target region based on the pressure loss value.
The method of claim 1, wherein
The medical image is a blood vessel tomographic image obtained by MRA, CTA, or DSA.
The method of claim 1, wherein
The blood flow rate is calculated from an equation modeled by a computer from Hagen-Poiseuille theory and a constant shear stress adjustment function of endothelial cells.
The method of claim 3, wherein
The tissue blood flow calculation coefficient is a method obtained by statistically processing a relationship between a vascular blood flow obtained from morphological imaging and a tissue blood volume obtained from functional imaging, and stored in the memory.
The method of claim 4, wherein
The step of calculating the pressure loss value is a method in which the computer applies the flow rate to an equation modeled on the basis of the Hagen Poiseuille theory.
The method of claim 1, wherein
A method further comprising calculating a blood vessel shape of each blood vessel from the medical image.
The method of claim 9, wherein
A method further comprising a step of physically calculating a blood flow in each blood vessel based on the calculated blood vessel shape.
The method of claim 2, wherein
The step of identifying the responsible blood vessel further comprises:
Displaying the medical image superimposed on the calculation result of blood flow analysis;
A user designates a target point of the analysis target region on the superimposed medical image;
Extracting a nearby blood vessel from a medical image designating the target point;
Having a method.
The method of claim 2, wherein
The method of calculating the streamline is a method of calculating a streamline by arranging a seed point at a position closest to the target point on the extracted nearest blood vessel.
A computer that processes a medical image including an analysis target region, identifies a responsible blood vessel that supplies blood to the analysis target region, and outputs a blood flow amount;
An apparatus for visualizing a tissue blood vessel characteristic, comprising: a tissue blood vessel characteristic calculating unit that calculates a tissue blood vessel characteristic of the analysis target region from the blood flow volume and superimposes or individually displays the tissue blood vessel characteristic on the medical image.
The apparatus of claim 13.
The responsible blood vessel identification unit
A nearest-neighboring blood-vessel extraction unit for the computer to extract the nearest-neighbor blood vessels for the analysis target region
A streamline calculator for calculating a streamline of blood flow through the nearest blood vessel by a computer;
A device further comprising: a responsible blood vessel determination unit that determines the responsible blood vessel based on the calculated streamline.
The apparatus of claim 13.
The tissue blood vessel characteristic is a tissue blood flow volume supplied to the analysis target region,
The tissue vascular characteristic calculation unit
A tissue blood flow calculation coefficient reading unit for reading a tissue blood flow calculation coefficient stored in a memory by a computer;
A tissue blood flow output unit that outputs a tissue blood flow per unit weight of the region to be analyzed by multiplying the blood flow rate of the responsible blood vessel by the computer-calculated tissue blood flow rate calculation coefficient; apparatus.
The apparatus of claim 14.
The tissue vascular characteristic is a erasure resistance in capillaries in the analysis target region,
The tissue vascular characteristic calculation unit further includes
A pressure loss value calculation unit that calculates a pressure loss value of the responsible blood vessel based on the blood flow volume of the responsible blood vessel;
And a erasing resistance calculating unit that calculates and outputs the erasing resistance of the analysis target region based on the pressure loss value.
The apparatus of claim 13.
The medical image is a blood vessel tomographic image by MRA, CTA, or DSA.
The apparatus of claim 13.
The blood flow rate is calculated from an expression modeled by a computer from Hagen Poiseuille theory and a constant shear stress adjustment function of endothelial cells.
The apparatus of claim 15.
The tissue blood flow rate calculation coefficient is obtained by statistically processing the relationship between the vascular blood flow rate obtained from morphological imaging and the tissue blood volume obtained from functional imaging, and is stored in the memory.
The apparatus of claim 16.
The said pressure loss value calculation part is a device which calculates by applying the said flow volume to the formula which the computer modeled based on the Hagen Poiseuille theory.
The apparatus of claim 13.
An apparatus further comprising a blood vessel shape calculation unit for calculating a blood vessel shape of each blood vessel from the medical image.
The apparatus of claim 21.
An apparatus further comprising a blood flow rate calculation unit that physically calculates a blood flow rate in each blood vessel based on the calculated blood vessel shape.
The apparatus of claim 14.
The responsible blood vessel identification unit further includes
A superimposed display unit that superimposes and displays the medical image on a calculation result of blood flow analysis;
A target designating unit for a user to designate a target point of the analysis target area on the superimposed medical image;
A neighboring blood vessel extraction unit that extracts a neighboring blood vessel from a medical image in which the target point is specified.
The apparatus of claim 14.
The streamline calculation unit calculates a streamline by arranging a seed point at a position closest to the target point on the extracted nearest blood vessel.