WO2016159379A1 - Apparatus and method for constructing blood vessel configuration and computer software program - Google Patents

Abstract

This apparatus has: an input permission determination unit that receives a medical image, determines the image property of the medical image and the degree of appropriateness of the medical image as such, and determines whether the medical image can be treated as an input image on the basis thereof; an image standardization unit that modifies/corrects a medical image that has been determined to be treatable as the input image on the basis of the determined image property and the determined degree of appropriateness to standardize the image quality of the medical image, and outputs a correction degree indicating the degree of the modification/correction; a blood vessel configuration constructing unit that constructs a three-dimensional blood vessel configuration on the basis of the standardized medical image; a blood vessel configuration quality evaluation unit that determines the blood vessel configuration construction accuracy of the constructed three-dimensional blood vessel configuration, and computes and outputs an overall quality evaluation of the constructed three-dimensional blood vessel configuration on the basis of the blood vessel configuration construction accuracy, the degree of appropriateness, and the correction degree; and a blood vessel configuration data output unit that outputs blood vessel configuration data representing the three-dimensional blood vessel configuration together with the configuration evaluation.

Description

Blood vessel shape construction apparatus, method and computer software program

The present invention relates to a blood vessel shape constructing apparatus for blood flow analysis by numerical fluid dynamics, a method thereof, and a computer software program.

Circulatory diseases include vascularization, hardening, and stenosis. These diseases involve lesions in the normal region due to the influence of the blood flow, and many of them are fatal due to subsequent progress, but their treatment is often life-threatening and difficult. In recent years, in order to elucidate and diagnose such intractable cardiovascular diseases and predict therapeutic effects, a technique for blood flow analysis of blood vessels including a lesion site has been demanded.

One method for performing this blood flow analysis is a method using computer processing using Computational Fluid Dynamics (CFD). Computational fluid dynamics is a technique for obtaining a fluid flow by computer analysis. For example, in Japanese Patent No. 5596866, three-dimensional blood vessel shape data indicating a blood vessel shape is constructed from medical image data imaged by a medical image pickup device or the like, and blood flow analysis is performed by numerical fluid dynamics based on the blood vessel shape. Technology is disclosed. Japanese Patent No. 559866 describes that a blood vessel treatment effect can be simulated by selecting information on a treatment method, correcting a blood vessel shape according to the information, and performing a blood flow analysis by numerical fluid dynamics.

In such blood flow analysis, the quality of the blood vessel shape to be calculated, that is, the quality of the three-dimensional shape data is important. This is because the quality of the blood vessel shape directly affects the accuracy of the result of blood flow analysis described above.

However, in the conventional blood vessel shape construction, each process such as selection of medical image data used for blood vessel shape construction and identification / extraction of a blood vessel region and a lesioned part is performed using various independent devices and software. In addition, since the user manually modified and confirmed medical image data and adjusted the parameters of the construction process, the blood vessel shape output would differ depending on the combination of the device and software and the degree of knowledge and experience of the user. There was a problem. That is, in the conventional blood vessel shape construction method, the blood vessel shape to be constructed is user-dependent, and the quality of the blood vessel shape tends to vary. And this variation in the quality of the blood vessel shape by the conventional blood vessel shape construction method may affect the accuracy of the results of blood flow analysis, especially in large-scale clinical experiments that compare enormous analysis results. It has been said.

In order for blood flow analysis to spread in the medical field, it is essential to guarantee the accuracy of the blood flow analysis results and to standardize and share them. It is important to have a technique for constructing a blood vessel shape with sufficient quality to guarantee the above.

Japanese Patent No. 5596866

The present invention has been made in view of such a situation, and an object of the present invention is to eliminate the user dependence by fully automatically processing each step of the blood vessel shape construction, and to construct the blood vessel shape for blood flow simulation. An object of the present invention is to provide a blood vessel shape constructing apparatus, a method thereof, and a computer software program that can perform quality control. *

According to the first main aspect of the present invention, a medical image is received, the image attribute of the medical image and the appropriateness as a medical image are determined, and based on these, a determination is made as to whether the image can be processed as an input image. An input availability determination unit and a medical image determined to be processable as the input image are corrected / corrected based on the determined image attribute and appropriateness, standardizing the image quality of the medical image, and the correction An image standardization unit that outputs a correction degree indicating the degree of correction, a blood vessel shape construction unit that constructs a three-dimensional blood vessel shape based on the standardized medical image, and a blood vessel shape of the constructed three-dimensional blood vessel shape A blood vessel shape quality evaluation unit that determines construction accuracy and calculates and outputs a total quality evaluation of the constructed three-dimensional blood vessel shape based on the blood vessel shape construction accuracy, the appropriateness, and the correction degree. , And wherein the blood vessel shape data indicating the three-dimensional vessel shape having a vessel shape data output unit that outputs together with the shape evaluation is provided.

Here, according to an embodiment of the present invention, the image attribute of the medical image is information including information on a manufacturer and a modality of the imaging device that captured the medical image and imaging conditions, The appropriateness is information including a noise level of a medical image, and the input availability determination unit performs the determination of input availability by referring to a predetermined template with the image attribute and appropriateness. In this case, it is preferable that the input availability determination unit determines a noise level by performing image analysis on a medical image. Further, in this case, it is preferable that the input availability determination unit calculates different types of noise components according to the image attributes by image analysis, and determines the noise level of each component based on the calculated noise components.

According to another embodiment of the present invention, the image standardization unit executes correction / correction for removing a specific noise component from the medical image based on the image attribute and appropriateness. is there.

According to still another embodiment, the image standardization unit performs isotropicization by equalizing spatial resolutions in the X direction, the Y direction, and the Z direction by interpolation in an XYZ coordinate system of medical images that are orthogonal to each other. The correction / correction is performed.

According to still another embodiment, the image standardization unit executes correction / correction for increasing the resolution by increasing the spatial resolution of the medical image by image interpolation.

According to still another embodiment, the image standardization unit performs image distortion correction derived from imaging and local blood vessel shape distortion correction, thereby standardizing the image quality. It is. In this case, the image standardization unit performs correction of the shape distortion of the local blood vessel shape with respect to the medical image determined as non-contrast MRA by the determination of the image attribute. The luminance information of the region corresponding to the inside and outside of the blood vessel on the medical image so as to eliminate the flow velocity dependence between the inside of the blood vessel and the outside of the blood vessel, which is a characteristic of non-contrast MRA. It is preferable to carry out based on the above. Furthermore, in this case, the image standardization unit detects a location where the flow velocity dependence of the medical image is stronger than a predetermined value based on luminance information of a location corresponding to the vicinity of the blood vessel wall on the medical image, and the blood vessel morphology information or blood It is preferable to correct the flow velocity dependence of the detected location based on flow information. Furthermore, in this case, it is preferable that the image standardization unit detects a location where the luminance gradient value in each cross section orthogonal to the blood vessel center line in the vicinity of the blood vessel wall is equal to or less than a threshold as a location having strong flow velocity dependency. . The image standardization unit corrects the flow velocity dependency of the image by applying the statistical model of the blood vessel at the detected location when the flow velocity dependency is corrected based on the morphological information of the blood vessel. It is preferable. In this case, it is preferable that the statistical model of the blood vessel is a mathematical model of a change in the cross section of the blood vessel in the blood vessel traveling direction obtained from the blood vessel shape database. Further, when correcting the flow velocity dependency based on the blood flow information of the blood vessel, the image standardization unit corrects the flow velocity dependency of the image by applying a blood flow model of the blood vessel at the detected location. It is preferable that In this case, the blood flow model of the blood vessel is a mathematical model of the blood flow information obtained by analyzing the actual blood flow information measured by the phase contrast MRI method or the flow velocity-dependent spatial distribution. Is preferred.

According to still another embodiment, the image standardization unit detects a blood vessel as a high-intensity signal for a medical image determined as a CTA (X-ray computed tomography image) by determining the image attribute. Correction / correction is performed to remove bones and / or calcifications that are easily misrecognized. In this case, the image standardization unit removes the bone using the anatomical features of the bone, or removes the bone using the statistical model of the blood vessel in combination. It is preferable that the change of the blood vessel cross section in the blood vessel traveling direction obtained from the shape database is mathematically modeled. Moreover, it is preferable that the said image normalization part removes calcification based on the brightness | luminance characteristic specific to calcification which is not in a bone or a blood vessel.

Further, according to still another embodiment, the blood vessel shape constructing unit analyzes the structure of the roughly divided blood vessel region, and a rough division unit for roughly dividing the blood vessel region by performing image analysis on the medical image. And a precision dividing unit that executes precise division of a blood vessel region based on the result of the structural analysis, and a detection unit that detects the presence or absence of adhesion of a specific blood vessel based on the result of the structural analysis. In this case, it is preferable that the blood vessel shape constructing unit re-executes the fine division of the blood vessel based on the determination of the presence or absence of the adhesion.

Further, according to still another embodiment, the blood vessel shape constructing unit analyzes the structure of the roughly divided blood vessel region, and a rough division unit for roughly dividing the blood vessel region by performing image analysis on the medical image. And a precision segmentation unit that performs precise segmentation of the blood vessel region based on the result of the structural analysis. The structural analysis obtains the center line of the blood vessel, and when the center line of the blood vessel is obtained, An orthogonal image is acquired at each point, the luminance value analysis of the orthogonal image is performed at each blood vessel, and a blood vessel region-specific determination of inside / outside blood vessel is performed from the luminance value analysis. The blood vessel region is divided based on the result. In this case, it is preferable that the inside / outside determination by the luminance value analysis is based on the half value of the maximum luminance value in the center line orthogonal image. Further, it is preferable that the blood vessel shape constructing unit enhances the accuracy of blood vessel region segmentation / extraction by recursively determining whether the blood vessel region is segmented / extracted.

According to a second main aspect of the present invention, a computer software program executed by a computer, each instruction stored in the following storage medium: the computer receives a medical image and an image of the medical image An attribute and an appropriateness as a medical image are determined, and an input propriety determination unit that determines whether the input image can be processed based on the attribute, and a medical image that is determined to be processable as the input image by the computer, An image standardization unit that corrects and corrects based on the determined image attribute and appropriateness, standardizes the image quality of the medical image, and outputs a correction degree indicating the degree of correction and correction, and a computer, A blood vessel shape constructing unit for constructing a three-dimensional blood vessel shape based on a standardized medical image, and a computer, the constructed three-dimensional blood A blood vessel shape quality evaluation unit that determines the blood vessel shape construction accuracy of the shape and calculates and outputs a total quality evaluation of the constructed three-dimensional blood vessel shape based on the blood vessel shape construction accuracy, the appropriateness, and the correction degree; A computer software program is provided, wherein the computer has a blood vessel shape data output unit that outputs blood vessel shape data indicating the three-dimensional blood vessel shape together with the shape evaluation.

According to a third main aspect of the present invention, there is provided a method executed by a computer, wherein the computer receives a medical image and determines an image attribute of the medical image and an appropriateness as a medical image. An input availability determination step for determining whether the input image can be processed based on them, and a medical image determined to be processed by the computer as the input image based on the determined image attribute and appropriateness An image standardization step of correcting and correcting and standardizing the image quality of the medical image and outputting a correction degree indicating the degree of correction and correction, and a computer configured to generate a three-dimensional blood vessel shape based on the standardized medical image And the computer determines the blood vessel shape construction accuracy of the constructed three-dimensional blood vessel shape and the blood vessel shape. A blood vessel shape quality evaluation step for calculating and outputting a total quality evaluation of the constructed three-dimensional blood vessel shape based on the construction accuracy, the appropriateness degree, and the correction degree, and a computer outputs blood vessel shape data indicating the three-dimensional blood vessel shape. And a blood vessel shape data output step for outputting together with the shape evaluation.

It should be noted that other features of the present invention other than those described above can be easily understood by those skilled in the art by referring to the “Description of Embodiments” and the drawings described below.

FIG. 1 is a diagram for explaining computational fluid dynamics. Fig. 2 shows the flow of blood flow analysis by computational fluid dynamics FIG. 3 is a diagram showing a blood vessel shape construction process FIG. 4 is a schematic configuration diagram showing an embodiment of the present invention. FIG. 5 is a diagram showing a blood vessel shape construction flow showing an embodiment of the present invention. FIG. 6 is a diagram for explaining factors that cause qualitative differences in medical images FIG. 7 is a block diagram showing the image discriminating unit. FIG. 8 is a block diagram showing the image standardization unit. FIG. 9 is a block diagram showing the blood vessel shape constructing unit. FIG. 10 is a diagram for explaining a phenomenon that looks as if it is adhered. FIG. 11 is a diagram for explaining the graphing of the blood vessel structure and the depth-first search of the graph. FIG. 12 is a diagram for explaining adhesion separation processing. FIG. 13 is a diagram for explaining operations of the shape measuring unit and the quality evaluating unit. FIG. 14 is a diagram for explaining quality determination of blood vessel shape data.

First, specific features of the present invention will be described.

(Characteristics of the Invention 1: Blood Vessel Shape Construction Device with Fully Automatic Control Function)
1-1. The present invention is a blood vessel shape structuring apparatus for blood flow analysis by numerical fluid dynamics, which eliminates user dependence by fully automating the process from inputting a medical image to outputting the blood vessel shape and quality evaluation. This is a blood vessel shape constructing device characterized by the above.
1-2. The feature 1-1 blood vessel shape constructing means includes: means for inputting a captured medical image; means for discriminating a medical image; means for standardizing a medical image; means for constructing a blood vessel shape; means for measuring a blood vessel shape; Means for quality evaluation, and means for outputting a blood vessel shape and a quality score.
1-3. The means for discriminating the medical image of feature 1-2 is the manufacturer, the means for discriminating the modality (device discrimination), the means for discriminating the imaging conditions (image discrimination), and discriminating whether the image is sufficient for blood flow analysis Means (input availability determination).
1-4. The means for determining whether or not the image is sufficient for the blood flow analysis of the characteristic 1-3 (input availability determination) determines whether it is possible to compare the conditions satisfying as a medical image that can withstand the blood flow analysis with a list of templates.
1-5. The means for standardizing the medical image of feature 1-2 includes means for removing noise (noise removal), means for isotropicizing the image by interpolation (isotropic), means for increasing the image resolution by interpolation (higher resolution) ) And means for correcting the image (image correction).
1-6. The means for constructing the blood vessel shape of feature 1-2 includes means for dividing a blood vessel into regions (region division), means for analyzing the structure of blood vessels (blood vessel structure analysis), means for detecting the presence or absence of adhesion (adhesion detection), Means for setting an area (area setting) and means for constructing a blood vessel shape (vascular shape construction)
1-7. The means for segmenting the blood vessel having the characteristic 1-6 is a means for extracting the blood vessel by performing inside / outside blood vessel determination based on the characteristic of the luminance value of the portion corresponding to the blood vessel on the image.
1-8. The means for structural analysis of the blood vessel of feature 1-6 is a means for performing labeling by dividing the blood vessel extracted in feature 1-7 by center line extraction into each blood vessel and lesioned part. 1-9. Performing features 1-7 and 1-8 first is the rough extraction of blood vessels. Based on the results of the rough extraction, it is precise to repeat the features 1-7 and 1-8 to correct the results. Let's say extraction. In precise extraction, adaptive blood vessel region segmentation in which the threshold value is adaptively changed for each luminance value of each blood vessel, and blood vessel structure analysis is performed again based on the result, or region segmentation is performed until a certain threshold value or less is reached. It is possible to perform recursive vascular region segmentation that repeatedly performs vascular structure analysis.
1-10. The means for detecting the presence or absence of adhesion 1-6 is a means for confirming the presence or absence of a blood vessel loop that cannot normally exist when the blood vessel is viewed as a whole. When a loop is confirmed, it is assumed that an artifact due to adhesion between blood vessels has occurred, and the adhesion is separated by performing precise extraction of feature 1-9 again.
1-11. The means for setting the region of the blood vessel of feature 1-6 includes the blood vessel / lesion labeled in feature 1-8 in the blood flow analysis by comparing the conditions to be satisfied as the region setting with a list of templates. It is a means for extracting only the region.
1-12. The means for constructing the shape of the blood vessel of the characteristic 1-6 is a means for forming the surface of the blood vessel from minute triangular elements by means of the marching cube method or the like with respect to the blood vessel set in the area of the characteristic 1-9.
1-13. The means for measuring the blood vessel shape of the characteristic 1-2 is a means for measuring the shape by creating an orthogonal cross section of the blood vessel at each point on the center line obtained in the characteristic 1-8.
1-14. The means for evaluating the quality of the blood vessel shape of the characteristic 1-2 calculates a quality score based on a luminance gradient in the vicinity of the blood vessel wall in the obtained blood vessel shape.

(Invention Feature 2: Blood Vessel Shape Construction Device with Image Standardization Function)
2-1. The present invention is a blood vessel shape constructing device for blood flow analysis by numerical fluid dynamics and having an image standardization function in which a difference in quality of medical images is corrected.
2-2. Means for standardizing the image of the characteristic 2-1 are means for removing noise (noise removal), means for isotropicizing the image by interpolation (isotropic), means for increasing the image resolution by interpolation (high resolution) And means for correcting an image (image correction).
2-3. The means for removing the noise of feature 2-2 is a means for removing different image noise depending on the manufacturer, modality, imaging condition, and imaging target.
2-4. The means for making the image isotropic by the interpolation of the feature 2-2 is means for making the spatial resolution of the medical image that can be different in the in-plane direction and the out-of-plane direction into a cube having no direction dependency.
2-5. The means for increasing the image resolution by the interpolation of the feature 2-2 is a means for artificially doubling the original spatial resolution of the image by the image interpolation.
2-6. The means for correcting the image of the feature 2-2 is a means for correcting a difference in the properties of different images depending on the manufacturer, modality, and imaging conditions.
2-7. In feature 2-6, in the case of non-contrast-enhanced MRA, there is means for correcting the flow velocity dependence of the luminance value, which is a property of the MRA image.
2-8. In feature 2-7, the means for correcting the flow velocity dependency includes a means for detecting a portion where the flow velocity dependency is strong, and a means for correcting the flow velocity dependency based on blood vessel form information or blood flow information.
2-9. In feature 2-8, the means for detecting a portion having a strong flow velocity dependency is based on a luminance gradient value in the vicinity of the blood vessel wall on each cross section orthogonal to the blood vessel center line. Since the portion where the brightness gradient value is low is a portion where the flow velocity is lowered, it becomes a factor that distorts the shape of the blood vessel wall.
2-10. In the feature 2-8, when the blood vessel shape information is used to correct the flow velocity dependency, the flow velocity dependency of the image is corrected by applying the statistical model of the blood vessel in the portion detected as having the flow velocity dependency. Is.
2-11. In feature 2-10, the statistical model of blood vessels is a mathematical model of how the cross section of a blood vessel changes in the direction of blood vessel travel obtained from the blood vessel shape database.
2-12. In feature 2-8, when blood flow information of a blood vessel is used to correct the flow velocity dependency, the flow velocity dependency of the image can be reduced by applying a blood flow model of the blood vessel at a location where the flow velocity dependency is detected. It is to correct.
2-13. In feature 2-12, the blood flow model of the blood vessel may use actual blood flow information measured by a phase contrast MRI method or the like, or a blood flow obtained by analyzing a flow velocity-dependent spatial distribution You may correct | amend from information.
2-14. In the characteristic 2-6, in the case of CTA, there is a means for removing bone and calcification that are easily misrecognized as blood vessels as a high luminance signal.
2-15. In the feature 2-14, as a means for removing the bone, a means using an anatomical feature of the bone may be used, or a statistical model of blood vessels shown in the feature 2-11 may be used in combination. Good.
2-16. In Feature 2-14, as a means for removing calcification, a means using luminance characteristics specific to calcification that is not present in bones or blood vessels may be used. For example, since calcification is a phenomenon that occurs locally, a high luminance signal is isolated in an island shape. Calcification can be detected and removed by extracting an isolated high-intensity signal.

(Invention Feature 3: Blood Vessel Shape Construction Device with Adaptive Blood Vessel Segmentation Function)
3-1. The present invention is a blood vessel shape structuring device for blood flow analysis by numerical fluid dynamics, which eliminates the influence of different luminance characteristics specific to a blood vessel region by performing blood vessel region adaptive determination in the blood vessel region extraction. This is a blood vessel shape constructing apparatus.
3-2. Feature 3-1 performs region segmentation and vascular structure analysis as a rough extraction of the segmentation of blood vessels. Region segmentation is performed with threshold values, and the center line of the blood vessel is obtained by blood vessel structure analysis. When the center line of the blood vessel is obtained, an orthogonal image is acquired at each point on the center line, and luminance values of the orthogonal image are analyzed for each blood vessel. Thereby, the variation of the luminance value in each blood vessel can be digitized.
3-3. The means for determining the inside / outside of the blood vessel from the luminance value analysis of the characteristic 3-2 includes means based on the half value of the maximum luminance value in the center line orthogonal image.
3-4. Means for determining whether or not the blood vessel part is specific from the luminance value analysis of feature 3-2 is based on the maximum point of the luminance gradient obtained by setting polar coordinates from the maximum luminance value in the center line orthogonal image. Including.
3-5. The means for determining whether or not the blood vessel part is specific from the luminance value analysis of the characteristic 3-2 includes means based on the maximum point of the luminance gradient obtained by setting polar coordinates from the center of gravity of the center line orthogonal image.

(Invention Feature 4: Blood Vessel Shape Construction Device Having Recursive Blood Vessel Segmentation Function)
4-1. The present invention is a blood vessel shape constructing device for blood flow analysis by numerical fluid dynamics, and is a blood vessel shape constructing device that improves the accuracy of blood vessel region extraction by recursively determining inside / outside blood vessels in blood vessel region extraction. .
4-2. The means for improving the accuracy of extracting the blood vessel region of the characteristic 4-1 includes means for performing rough extraction of blood vessels and means for performing precise extraction of blood vessels.
4-3. In the feature 4-2, means for roughly extracting a blood vessel includes means for extracting a rough blood vessel shape by rough inside / outside determination and means for extracting a rough center line from the rough blood vessel shape.
4-4. In the feature 4-3, the means for extracting the rough blood vessel shape by the rough inside / outside determination is a means for extracting the rough blood vessel shape by binarizing the image using a predetermined value or a reference value calculated from the luminance value histogram. It is.
4-5. In the feature 4-2, the means for precise blood vessel extraction includes means for creating an orthogonal cross section at each point of the coarse center line, means for creating a center line orthogonal image by image interpolation, and analyzing the luminance value of the center line orthogonal image A means for making a precision blood vessel inside / outside determination from luminance value analysis, and a means for creating a precision center line from the result of the inside / outside precision blood vessel determination
4-6. Means for performing precise blood vessel inside / outside determination based on the luminance value analysis of feature 4-5 includes means based on the half value of the maximum luminance value in the center line orthogonal image.
4-7. Means for performing precise blood vessel inside / outside determination from the luminance value analysis of feature 4-5 includes means based on the maximum point of the luminance gradient obtained by setting polar coordinates from the maximum luminance value in the center line orthogonal image.
4-8. Means for performing the precise blood vessel inside / outside determination from the luminance value analysis of feature 4-5 includes means based on the maximum point of the luminance gradient obtained by setting polar coordinates from the center of gravity of the center line orthogonal image.
4-9. The rough extraction and fine extraction of the feature 4-2 may be performed in a single stage or repeatedly.

(Feature 5 of the invention: a blood vessel shape constructing device having an automatic region setting function)
5-1. The present invention is a blood vessel shape constructing device for blood flow analysis by numerical fluid dynamics, and has a function of automatically setting a blood flow analysis region for an extracted blood vessel.
5-2. The means for automatically setting the blood flow analysis region of the characteristic 5-1 includes means for analyzing the blood vessel structure and means for determining whether or not to include in the analysis region.
5-3. The means for analyzing the structure of the blood vessel of the characteristic 5-2 is a means for determining the main blood vessel, a means for determining the subordinate blood vessel by tracking the travel of each blood vessel from the center line of the main blood vessel and comparing it with the anatomical template, And means for labeling the blood vessel names on the subordinate blood vessels.
5-4. A means for determining whether or not to include in the analysis region of the feature 5-2 is performed by referring to a template in which a set of main blood vessels and subordinate blood vessels is listed in advance as a blood flow analysis region.

(Characteristic 6 of the Invention: Blood Vessel Shape Construction Device Having Adhesion Detection and Separation Functions)
6-1. The present invention is a blood vessel shape constructing device for blood flow analysis by numerical fluid dynamics, and has a function of automatically detecting and separating the presence or absence of blood vessel adhesion when evaluating a blood vessel shape obtained from a medical image. It is a shape construction device.
6-2. The feature 6-1 means for automatically detecting and separating the presence or absence of blood vessel adhesion includes means for detecting the presence or absence of a blood vessel loop in the blood vessel structure analysis, and means for redoing the blood vessel shape construction from the region division when adhesion is detected. .
6-3. In feature 6-2, the means for detecting the presence or absence of a vascular loop is performed by preparing data associating a vascular center line with a center line end point and detecting whether there are a plurality of blood vessels sharing the center line end point. .
6-4. In feature 6-2, when adhesion is detected, the means for reconstructing the blood vessel shape from the region division is performed by increasing the accuracy of the blood vessel inside / outside determination at the corresponding location.
6-5. In the feature 6-4, the means for improving the accuracy of the determination of the inside / outside of the blood vessel is based on applying the inside / outside blood vessel determination method on the premise that the blood vessels that have adhered to each other do not overlap each other.

Next, an embodiment of the present invention will be specifically described with reference to the drawings.

As described above, the present invention relates to a blood vessel shape constructing apparatus for blood flow analysis by computational fluid dynamics (CFD), a method thereof, and a computer software program, and in particular, a medical image as an input. This is a device that generates a blood vessel shape that does not depend on the user by fully automating the steps up to outputting the blood vessel shape model and quality evaluation.

(Blood flow analysis using computational fluid dynamics)
In order to simplify the description of this embodiment, the concept of blood flow analysis using numerical fluid dynamics will be described here.

In computational fluid dynamics, fluid flow is calculated and output by computer analysis. More specifically, an approximate solution is obtained by sequential operation by replacing a governing equation describing a flow (continuous equation, Navier-Stokes equation) with an algebraic equation.

In this embodiment, as shown in FIG. 1, four types of blood vessel shape 1, blood physical property 2, boundary condition 3, and calculation condition 4 are used as inputs. Based on these inputs, a computational fluid analysis (CFD) is executed, and a pressure field 5 and a velocity field (also referred to as “flow field”) 6 in the space are output. In this example, the pressure field 5 and the velocity field 6 in time and space are calculated by solving the governing equation as a time evolution type.

FIG. 2 shows the flow of blood flow analysis by the above numerical fluid dynamics. In the flow of blood flow analysis, first, three-dimensional shape data (surface mesh) representing a blood vessel shape is constructed from a medical image obtained by X-ray CT or the like (FIGS. 2A to 2B). Then, for the constructed three-dimensional shape data of the blood vessel shape, the blood physical property 2, the boundary condition 3, the calculation condition 4, etc. are set, and the numerical fluid analysis calculation is executed (FIGS. 2 (c) to (g)). . Here, the blood property 2 is specifically the density and viscosity of blood. Specifically, the boundary condition 3 is a flow rate at the end face of each blood vessel, a constraint condition at the blood vessel wall surface, and the like. For example, when a blood vessel wall is fixed, the so-called non-slip condition is generally applied in which the velocity is zero by ignoring fluid slip on the wall, but this is also part of the boundary condition. is there. Examples of the calculation condition 4 include generation of a calculation grid for a blood vessel shape to be calculated, equation discretization regarding an equation solving method, simultaneous equation solving method, and the like, and these conditions may be set in a hierarchy.

Next, with reference to FIG. 3, the concept of the construction process of the three-dimensional shape data representing the blood vessel shape will be described. In the blood vessel shape construction, first, a medical image is acquired (FIG. 3A). Next, the inside / outside of the blood vessel is determined from the medical image to divide the blood vessel and other regions (FIG. 3B). Next, a region for blood flow analysis is set (FIG. 3C). Thereafter, the surface shape of the blood vessel wall is constructed by the marching cube method or the like (FIG. 3D). That is, a transition is made from the voxel space of the image to the polygon space. Therefore, at this time, the blood vessel wall surface is constructed from minute polygonal elements such as minute triangular elements.

In such a blood vessel shape construction, when each step shown in FIG. 3 is performed by an independent system or software, the blood vessel shape to be constructed may vary due to a difference in processing selected by the user. That is, user dependency cannot be excluded. In order to construct a blood vessel shape in which user dependence is eliminated from various medical images, for example, the following points are given as specific problems.
(1) The quality of the medical image to be input varies depending on the manufacturer, modality, imaging conditions, and imaging target, but it is not clear what medical image can achieve blood flow analysis that can guarantee accuracy. .
(2) Although the luminance value in the blood vessel changes depending on the location, it is not clear what kind of inside / outside blood vessel determination method can achieve blood flow analysis with guaranteed accuracy.
(3) The blood flow analysis result changes depending on the region setting method, and it is not clear what blood vessel region setting method can achieve the blood flow analysis that can guarantee the accuracy.
(4) Although the accuracy of shape construction itself varies depending on the size of the minute triangle, it is not clear what shape construction method can achieve blood flow analysis that can guarantee accuracy.

As described above, the quality of the blood vessel shape, that is, the shape reproducibility directly affects the blood flow analysis result. Therefore, it is important to build a high-quality blood vessel shape with high reliability, and the blood vessel shape construction device in this embodiment guarantees the accuracy of the results of blood flow analysis without user dependence by the processing described below. To build a blood vessel shape of quality.

(Outline of this embodiment)
In view of the above problems, the inventor of the present application has made an invention from the following viewpoints (I) to (VI).

(I) Blood vessel shape construction device with fully automatic control function
A first feature of the present invention is a blood vessel shape constructing apparatus for blood flow analysis by numerical fluid dynamics, which takes a medical image as an input and fully automates a process until outputting a blood vessel shape and a quality evaluation. It is a blood vessel shape constructing apparatus characterized by eliminating user dependence.
The present invention solves the problem by taking a new viewpoint of performing absolute evaluation based on blood flow information obtained from blood flow analysis results. More specifically, the wall shear stress acting on the blood vessel wall is an important index, and each step of blood vessel shape construction is optimized based on keeping the wall shear stress within a certain range to achieve overall quality control. We have developed a dedicated device for constructing blood vessel shapes. According to the present invention, it is possible to provide a blood flow analysis result with accuracy guaranteed without depending on the user's experience and knowledge.

(II) Blood vessel shape construction apparatus having image standardization function
The second feature of the present invention is to provide an image standardization function in a blood vessel shape construction apparatus that eliminates user dependence by fully automatically processing each step of blood vessel shape construction and enables quality control of blood vessel shape construction. Is.
The quality of medical images is known to vary depending on manufacturer, modality, imaging conditions, and imaging target, but is not standardized. In the conventional blood flow analysis, the blood vessel shape is constructed without correcting the input medical image. For example, in MRA, the signal intensity is an image that emphasizes the flow velocity dependence, and in CTA and DSA, the contrast agent concentration dependence is emphasized. In the former case, the blood vessel shape is distorted according to the flow velocity distribution. . The essential cause of this is that when the images are compared as shape information, the evaluation is merely relative evaluation and accuracy evaluation based on the true value cannot be achieved. The present invention solves the problem by taking a new viewpoint of performing absolute evaluation based on blood flow information obtained from blood flow analysis results. More specifically, wall shear stress acting on the blood vessel wall is an important index, and a medical image correction method has been developed on the basis of keeping the wall shear stress within a certain range. According to the present invention, it is possible to provide a blood flow analysis result with accuracy guaranteed without depending on the user's experience and knowledge.

(III) A blood vessel shape constructing device having an adaptive blood vessel region dividing function
The third feature of the present invention is that an adaptive blood vessel region segmentation function in a blood vessel shape constructing apparatus that eliminates user dependence and enables quality control of blood vessel shape construction by fully automatically processing each step of blood vessel shape construction. Is to provide.
When a blood vessel is extracted from a medical image by segmentation, a blood vessel inside / outside determination is performed. It is known that the signal intensity in the blood vessel varies depending on the site. More specifically, the signal intensity is high in a main blood vessel having a relatively large diameter. Therefore, if the internal and external criteria for the main blood vessel are applied to the entire vascular system, the subordinate blood vessels will be underestimated. Moreover, if it carries out with respect to a dependent blood vessel, it will overestimate a main blood vessel. The present invention solves the problem by taking a new viewpoint of performing absolute evaluation based on blood flow information obtained from blood flow analysis results. More specifically, the wall shear stress acting on the blood vessel wall is an important index, and an adaptive blood vessel segmentation method has been developed based on keeping the wall shear stress within a certain range. According to the present invention, it is possible to provide a blood flow analysis result with accuracy guaranteed without depending on the user's experience and knowledge.

(IV) A blood vessel shape constructing device having a recursive blood vessel region dividing function
A fourth feature of the present invention is a recursive blood vessel region segmentation function in a blood vessel shape constructing apparatus that eliminates user dependence by fully automatically processing each step of blood vessel shape construction and enables quality control of blood vessel shape construction. Is to provide.
When a blood vessel is extracted from a medical image by segmentation, it is necessary to determine whether the blood vessel is inside or outside. The inside / outside blood vessel determination is performed by setting a certain threshold value with respect to a change in signal intensity from inside the blood vessel to the outside. Here, the traveling direction of the blood vessel is arbitrary, and a phenomenon such as being close to being perpendicular to the image capturing direction in a certain part and being nearly parallel in another part appears. Since the signal intensity varies depending on the blood vessel running direction, it is desirable to extract the shape as seen from the blood vessel running direction. However, no highly accurate blood vessel inside / outside determination method considering the blood vessel running direction has been reported so far. The present invention solves the problem by taking a new viewpoint of performing absolute evaluation based on blood flow information obtained from blood flow analysis results. More specifically, it is clear that the wall shear stress acting on the blood vessel wall can be kept within a certain range by performing the blood vessel region extraction stepwise from the rough extraction to the precise extraction based on the center line. It was. That is, a recursive blood vessel region segmentation method that recursively determines inside / outside blood vessels has been developed. According to the present invention, it is possible to provide a blood flow analysis result with accuracy guaranteed without depending on the user's experience and knowledge.

(V) Blood vessel shape construction device having automatic region setting function
The fifth feature of the present invention is to provide an automatic region setting function in a blood vessel shape construction device that eliminates user dependence and enables quality control of blood vessel shape construction by fully automatically processing each step of blood vessel shape construction. To do.
It is necessary to set an analysis region for the blood vessel shape extracted by region division. Until now, the analysis area has been arbitrarily determined by the user, and this has been a factor in causing variation in analysis results. The present invention solves the problem by taking a new viewpoint of performing absolute evaluation based on blood flow information obtained from blood flow analysis results. More specifically, the wall shear stress acting on the blood vessel wall is an important index. Based on the fact that the wall shear stress falls within a certain range, the main blood vessels to be included in the analysis region are determined according to the site of the lesion. A template that shows dependent blood vessels was created, and an automatic blood vessel region setting method that eliminates user dependency by automating region settings based on the template was developed. According to the present invention, it is possible to provide a blood flow analysis result with accuracy guaranteed without depending on the user's experience and knowledge.

(VI) Blood vessel shape construction device having adhesion detection and separation functions
The sixth feature of the present invention is that the adhesion detection and separation function in the blood vessel shape constructing apparatus that eliminates the user dependence by fully automatically processing each step of the blood vessel shape construction and enables quality control of the blood vessel shape construction. It is to provide.
In some cases, the blood vessel shape extracted by region segmentation may cause adhesion of blood vessels due to artifacts caused by image processing even though there is no original adhesion. In this case, the user visually confirms the presence or absence of adhesion and separates blood vessels by manually processing the image of the adhesion site. For this reason, arbitraryness by the user occurred, which was a cause of variation in analysis results. Therefore, in the present invention, a blood vessel shape construction method has been developed that automates the detection and separation of blood vessel adhesions. According to the present invention, it is possible to provide a blood flow analysis result with accuracy guaranteed without depending on the user's experience and knowledge.

Hereinafter, the embodiment of the present invention will be described in more detail.

(Best Embodiment)
FIG. 4 is a schematic configuration diagram showing the blood vessel shape constructing apparatus according to this embodiment.

The blood vessel shape constructing apparatus 10 includes a program storage unit 60 and data storage units 70 and 71 connected to a bus 50 to which a CPU 18, a RAM 19 and an input / output unit 20 are connected. In this apparatus, the bus 50 may be further connected to other devices that store various external reference data.

The program storage unit 60 includes an image input unit 11, an image determination unit 12, an image standardization unit 13, a shape construction unit 14, a quality evaluation unit 15, a shape measurement unit 16, and a blood vessel shape data output unit 17. I have. The data storage unit 70 stores medical image data input from the input / output unit 20. The data storage unit 71 stores the blood vessel shape data 21, the image appropriateness degree 22, the image correction degree 23, the lesion site / type 24, the blood vessel shape measurement data 25, and the quality score 26.

The configuration requirements (the image input unit 11, the image determination unit 12, the image standardization unit 13, the shape construction unit 14, the quality evaluation unit 15, the shape measurement unit 16, and the blood vessel shape data output unit 17) are actually Is configured by computer software stored in a storage area of a hard disk, and is configured to function as each component of the present invention by being called by the CPU 18 and expanded and executed on the RAM 19.

FIG. 5 shows the flow of blood vessel shape construction by this apparatus. Hereafter, each process shown by this figure is demonstrated in order.

(Medical image acquisition (step S1))
In step S1, a medical image is input to the image input unit 11 of this system.

In this embodiment, the input medical image is an apparatus capable of acquiring a tomographic image of a target blood vessel site such as MRA (magnetic resonance image), CTA (X-ray computed tomography image), DSA (angiographic image), etc. In addition, it may be obtained by various devices capable of acquiring image data in a target blood vessel site, such as US (ultrasonic image), IVUS (intravascular ultrasonic image), OCT (near infrared image), and the like. .

The target blood vessel site may be, for example, a cerebral artery, a coronary artery, a carotid artery, an aorta, or another target blood vessel site of a subject.

As described above, the quality of medical images, that is, image characteristics, varies depending on the device used, imaging target, imaging conditions, and the like. In the following, factors that cause qualitative differences in medical images will be systematically described.

Medical images include a standard indicating image attributes called DICOM (Digital Imaging and Communication in Medicine), and many medical images conform to this standard. However, there is almost no standardization regarding the quality of medical images. Therefore, if appropriate correction processing is not performed on the medical images captured by the various devices, different blood vessel shapes may be constructed depending on the image characteristics of the medical images. That is, if a blood vessel shape is constructed from medical images obtained with different devices and imaging conditions by the method shown in FIGS. 3A to 3D, even if the same blood vessel is imaged, the shape is the same. However, the results of blood flow analysis are expected to change and affect the accuracy of the analysis results.

Factors that make a difference in the image characteristics of such medical images are broadly divided into “apparatus characteristics 31” and “imaging characteristics 32” as shown in FIG. 6, and further, the apparatus characteristics 31 are “maker 33” and “modality”. 34 ”and the imaging characteristic 32 can be divided into“ imaging condition 35 ”and“ imaging target 36 ”.

Here, “Manufacturer 33” is a company developing imaging equipment. The “modality 34” is an imaging device, for example, an apparatus such as the above-described MRA, CTA, DSA, US, IVUS, OCT or the like. Differences in measurement principles among various imaging devices strongly affect image characteristics as will be described later.

The “imaging condition 35” can be subdivided into (1) measurement condition and (2) injection condition. The “measurement condition” means the intensity of irradiation waves such as a magnetic field and X-rays, and the resolution of space and time. The measurement conditions include an image construction method for acquiring a two-dimensional tomographic image group as an image set. This “image construction method” differs depending on the measurement principle of the imaging device. That is, it depends on the modality. For example, in the case of MRA, a tomographic image can usually be acquired directly because imaging and measurement are performed while scanning in the body axis direction. On the other hand, in the case of CTA and DSA, a tomographic image is acquired by converting measurement data using a special reconstruction function because a helical method or a tomography method is used. In the case of IVUS or OCT, since the inside of the blood vessel is scanned and imaged while rotating the sensor at the catheter tip, a tomographic image in the blood vessel traveling direction is acquired by correcting the difference in the local coordinate system at each imaging position. . Furthermore, the image construction method includes a subtraction method that removes bone and calcification by subtracting the images before and after contrasting in imaging using a contrast agent. The subtraction method is extremely useful in blood vessel shape construction because it can eliminate the effects of bone and calcification. The “injection condition” is an injection condition of a contrast medium injected from a vein or an artery to make a blood vessel image clear. The type, concentration, injection speed, injection time, etc. of the contrast agent affect the image quality.

The “imaging target 36” can be subdivided into “patient situation” and “imaging site”. “Patient status” relates to the degree of vascular lesions and the presence or absence of treatment. Examples of the “vascular lesion” include those caused by arteriosclerosis. When arteriosclerosis progresses, calcification is often accompanied, but in images containing such arteriosclerotic sites, local halation may occur due to calcification (or high calcification) Affects the construction of blood vessel shape. “Presence / absence of treatment” is, for example, whether or not a treatment device such as a stent, coil, clip, or the like is placed in the body. In a patient who has received such indwelling treatment of a treatment device, noise may be generated around the treatment device and the image quality may be reduced. The “imaging part” indicates a factor that affects the image quality in a part-dependent manner. For example, when the vicinity of the nasal cavity is imaged by MRA, the magnetic field tends to vary spatially due to the air layer. Further, when imaging around a hard tissue such as a bone by CTA, it is difficult to distinguish between the bone and the blood vessel.

As described above, factors that cause differences in image characteristics are affected by an extremely large number of parameters, and qualitative image correction of medical images according to these factors is indispensable for ensuring the accuracy of blood flow analysis.

Therefore, image correction according to various factors is performed in the following steps.

(Image discrimination process (step S2))
In this step S2, the image discriminating unit 12 encodes and digitizes two pieces of “image attribute information” and “image analysis information” as appropriate image information, and processes them as input images for blood flow analysis based on this. It is determined whether or not input is possible.

FIG. 7 is a configuration diagram of the image discrimination unit 12.

The image determination unit 12 has a configuration in which a medical image is input and includes (1) a device characteristic determination unit, (2) an imaging characteristic determination unit, and (3) an image appropriateness determination unit. Outputs image suitability.

First, when a medical image is input (step S2-0), device characteristics are determined as image appropriate information by the manufacturer determination unit and the modality determination unit (steps S2-1-1 and S2-1-1-). 2). As a result, the manufacturer and modality of the device that has given the medical image are determined. The manufacturer and modality information is added to the medical image, so that it is performed based on the information.

Next, the imaging characteristics are discriminated by the imaging condition discriminating unit and the imaging target discriminating unit (step S2-2-1 and step S2-2-2). Information on the imaging characteristics is added to the medical image, and is performed based on the information. Information regarding the imaging conditions is given as attribute information to the medical image, and is performed based on the attribute information (step S2-2-1).

The imaging target discriminating unit performs image analysis, discriminates various types of noise, quantifies and outputs as appropriate image information (step S2-2-2). Specifically, (1) device-derived ε ₁ , (2) contrast agent-derived ε ₂ , (3) blood vessel-derived ε ₃ , (4) hard tissue-derived ε ₄ , (5) site-derived ε ₅ , (6) Each noise of the treatment-derived ε ₆ is calculated. In this embodiment, noise is defined as an error factor in blood flow analysis.

A specific calculation method will be described below.
(1) Device-derived noise Device-derived noise is device-specific background noise included in the background of an image. There are random noise that is uniformly distributed over the entire image and bias noise that appears in a pattern such as a stripe pattern in the image local area. In this embodiment, the features of these noises are digitized by a phantom test image. More specifically, the random noise is quantified as a high frequency component by Fourier transform, and the bias noise is quantified as a low frequency component for output and discrimination.
(2) Contrast-agent-derived noise Contrast-agent-derived noise is detected as uneven brightness values due to poor mixing of contrast agent and blood. When the contrast medium and the blood are well mixed, the change in the brightness value of the contrast medium can be distinguished from each other because it shows a certain feature regardless of the location. For example, when there is no unevenness in the luminance value, it is equivalent in the direction orthogonal to the blood vessel and gradually decreases in the traveling direction. The poor mixing of the contrast agent and the blood is quantified by analyzing the signal intensity of the brightness value of the contrast agent.
(3) Blood vessel-derived noise Blood vessel-derived noise is noise related to vascular shape deviation or adhesion. In a lesion such as an aneurysm or stenosis, the shape of the blood vessel deviates from the normal value. When a blood vessel shape deviating excessively is used, the accuracy of blood flow analysis itself may not be guaranteed. For example, it is difficult to accurately quantify the stenotic part itself in an excessively stenotic blood vessel. The same applies to adhesions. The blood vessel-derived noise is calculated based on the quantification of the blood vessel shape, and an area that is inappropriate for blood flow analysis is defined as blood vessel-derived noise and specified.
(4) Hard tissue-derived noise Hard tissue-derived noise is noise related to bone and calcification. Bone and calcification are observed as a high luminance signal exceeding the luminance value of the contrast agent. Because of the high luminance signal, the signal tends to be misrecognized as a part of the blood vessel lumen even though it is inside or outside the blood vessel wall. Therefore, here, signals from bone and calcification are detected as noise. Bone is an anatomically continuous high intensity signal, and calcification is a discontinuous high intensity signal isolated from the surroundings. Based on the difference in signal strength characteristics, the noise between the two is detected and quantified.
(5) Site-derived noise Site-derived noise is detected based on the degree of luminance change in the region around the blood vessel. For accurate blood vessel shape construction, it is desirable that there is sufficient contrast around the blood vessel as compared to the inside of the blood vessel. On the other hand, there are noise components such as hard tissue such as bone and calcification, soft tissue such as brain parenchyma around the blood vessel, and air layer around the nasal cavity. Here, the part-derived noise is quantified and discriminated by analyzing the change in the luminance value in the peripheral region of the blood vessel.
(6) Treatment-derived noise Treatment-derived noise is defined as noise specific to a treatment device when, for example, various treatment devices such as stents, coils, clips, etc. are already implanted in the body, the irradiation wave from the diagnostic device is reflected on the treatment device. It is a phenomenon that appears. There is a feature observed as a high-intensity signal having a spatially constant pattern, and noise is detected by detecting the pattern and quantifying the signal intensity.

Next, image appropriateness is determined by an image appropriateness calculating unit and an input availability determining unit (steps S2-3-1 and S2-3-2). The image appropriateness calculation unit calculates the above-mentioned total noise as a noise amount (step S2-3-1). That is, the total noise amount is

It becomes. Here, α and β are coefficients and multipliers, which are specific values for each noise. In this embodiment, these values are optimized according to an increase in the number of cases by machine learning. When machine learning is performed in the hospital, the degree of optimization differs depending on the facility due to the difference in the number of cases. Therefore, the image analysis results are transferred to an external data server outside the hospital via a dedicated line. By using machine learning in a multi-facility network through an external data server, homogenization of values between facilities is achieved. The coefficients and multipliers α and β obtained here are appropriately written and updated in the image analysis information template, and are used for the calculation of the sum of noise amounts and the calculation of the image appropriateness described below.

On the other hand, the input availability determination unit performs input availability determination based on the aforementioned image attribute information (input availability determination template 40 based on image attribute information) and image analysis information (input availability determination template 41 based on image analysis information) (step) S2-3-2).

An input permission determination template (hereinafter referred to as “image attribute information template”) 40 based on image attribute information is a template of the manufacturer, modality, and imaging conditions that can be input. If it is impossible, it is a template having a function of outputting 1.

Image analysis information template (hereinafter referred to as “image analysis information template”) 41 is a template that is normalized by the total noise amount, that is, has a function of outputting a value less than 1. Specifically, the coefficients α and β for each noise type and the threshold value for the total noise amount are stored with respect to the total noise amount indicated by the above formula. And as mentioned above, α and β are constantly updated by machine learning. By comparing this threshold value with the amount of noise, the image appropriateness is determined by ranking A, B, and C in this embodiment.

The input availability determination unit determines whether or not a medical image can be input based on the image attribute and the image appropriateness. For example, with respect to the image attributes, those having a specific manufacturer or modality are determined to be input rejected. As for the image appropriateness, those of rank C are determined to be input refusal.

Thereafter, the image determination unit 12 outputs the medical image and the image appropriateness level (S2-4). In this embodiment, the medical image determined to be input and the image appropriateness level are output. However, the present invention is not limited to this. For example, even when it is determined that the input is rejected, the image appropriateness level may be output. . Further, each calculated noise amount may be output.

(Image standardization process (step S3))
FIG. 8 is a configuration diagram of the image standardization unit 13.

The image standardization unit 13 receives a medical image and image appropriate information (including image appropriateness) and has a configuration including a noise removal unit, an isotropic unit, a high resolution unit, and an image correction unit. The standard medical image, the image appropriateness level, and the image correction level are output.

First, when a medical image and image appropriateness are input to the image standardization unit 13 (S3-0), the noise removal unit removes noise included in the original medical image (step S3-1). The noise referred to here is (1) device-derived, (2) contrast agent-derived, (3) blood vessel-derived, (4) hard tissue-derived, (5) site-derived, or (6) treatment-derived. Since the specification of the noise portion has already been completed as the determination of the image appropriate information by the image determination unit 12, the noise removal unit removes various noise components from the specific portion of the original image.

The above noise is specified in either the spatial domain or the frequency domain. In this embodiment, the spatial domain indicates that an image is directly analyzed, and the frequency domain indicates that the image is subjected to frequency analysis using Fourier transform or wavelet analysis. The noise removing unit removes a noise component in either the spatial domain or the frequency domain according to the difference in various noise quantification methods.

Next, the isotropic unit and the high resolution unit perform isotropic (step S3-2) and high resolution (step S3-3) by image interpolation, respectively. In isotropicization, the spatial resolution anisotropy is corrected, and an image composed of cubic voxels is acquired. Higher resolution increases the apparent spatial resolution.

Next, the image correction unit performs image correction in two stages (step S3-4).

First, as the primary image correction, the device characteristic and the imaging characteristic are corrected (step S3-4-1). In this correction, device characteristics, that is, image distortion derived from the device, and imaging characteristics, that is, image distortion derived from the imaging are corrected.

Equipment-derived image distortion is distortion that the entire image is caused by differences in manufacturer or modality. For example, MRA acquires slice images while scanning the imaging section in the body axis direction, CTA and DSA scan tomographic images while rotating the device, and IVUS and OCT scan catheter type image sensors placed in blood vessels. While acquiring the slice image. These modality differences cause device-specific image distortion. In this embodiment, such image distortion characteristics are quantified in advance from an image test using a phantom, and distortion correction is performed using a correction example that is optimized to minimize image distortion from the test image using the phantom.

Also, image distortion derived from imaging is distortion that the imaging condition or imaging target gives to the entire image. The image distortion due to the imaging condition includes the image distortion method. This distortion is also corrected using a correction matrix based on a phantom experiment as described above.

After completing the primary image correction, the process proceeds to the secondary image correction described later. The primary image correction is for distortion of the entire image, but the secondary image correction is for local blood vessel shape distortion. In the blood vessel shape construction in this embodiment, the region inside and outside the blood vessel is divided as will be described later. This segmentation is to determine inside / outside by applying some standard to the change in luminance value from inside the blood vessel to outside the blood vessel, but the change in luminance value in the boundary region inside / outside the blood vessel It is desirable to be constant regardless of the position inside. This is because if the change in the luminance value near the boundary between the inside and outside of the blood vessel (boundary region) varies depending on the position in the target blood vessel, local distortion may occur in the constructed blood vessel shape.

For example, a change in luminance value in the boundary region inside and outside the blood vessel may be different between a linear blood vessel portion and a curved blood vessel portion, and based on such a medical image without any correction processing. When the blood vessel shape is constructed, it is predicted that different distortions are given to the constructed blood vessel shape depending on the position. More specifically, in the case of non-contrast-enhanced MRA, the luminance value itself is dependent on the flow velocity, so that the luminance value changes between the outer periphery and the inner periphery of the blood vessel, and the constructed blood vessel shape may be distorted. . Further, in CTA and DSA using a contrast agent, the luminance value is concentration-dependent. That is, in CTA and DSA using a contrast agent, the luminance value does not change due to the flow velocity as in the case of non-contrast MRA, but the contrast agent has a higher specific gravity than blood and is uniformly dispersed in the blood vessel cross section. Therefore, there may be a place dependency in the change of the luminance value in the boundary region inside and outside the blood vessel due to poor mixing of the contrast agent and blood.

Therefore, the image correction unit calculates a luminance gradient from the luminance value around the blood vessel wall of the medical image on which the first image correction has been performed (step S3-4-2), and the correction target is based on the amount of change in the luminance gradient. Is detected (step S3-4-3). The image correction unit calculates the luminance gradient value with respect to the two axes of the major axis and the minor axis. Here, the long axis is a coordinate axis taken in the blood vessel traveling direction, and the short axis is a coordinate axis taken in the blood vessel orthogonal direction. The method for calculating the vessel running direction and the orthogonal direction will be described in detail in the shape construction (step 4). In this embodiment, a luminance gradient value inside and outside the blood vessel is calculated, and a portion where the luminance gradient value is not more than a threshold is detected as a correction required portion. This is because the portion where the luminance gradient value is low is a portion where the flow velocity is reduced, which causes the shape of the blood vessel wall to be distorted.

Next, the secondary image correction is performed based on the morphological information or the blood flow information for the portion that needs to be corrected (step S3-4-4). In this embodiment, the morphological information and blood flow information are a blood vessel statistical model and a blood flow statistical model calculated as statistical average values. In the blood vessel statistical model, a blood vessel is a tapered tube with respect to the flow direction, and its shape change is replaced with a mathematical model. The blood flow statistical model is obtained by replacing the change pattern of the luminance value that appears in a blood flow dependent manner with a mathematical model. For example, the blood flow statistical model is obtained by mathematically modeling blood flow information obtained by analyzing actual blood flow information measured by a phase contrast MRI method or a flow velocity-dependent spatial distribution. These statistical models are acquired by machine learning from a standard medical image database. In this embodiment, the standard medical image database is composed of a multi-facility shared network arranged outside the hospital in order to eliminate differences between facilities.

When the secondary image correction is completed, the standard medical image, the image appropriateness level, and the image correction level are output (step S3-4-5). In this embodiment, the image correction degree is the size of the area of the correction portion.

(Shape construction (step 4))
FIG. 9 is a configuration diagram illustrating the blood vessel shape constructing unit 14.

The input is the medical image standardized in the above process, the image appropriateness, and the image correction, and the output is the blood vessel shape data, the lesion site / type, the image appropriateness, and the image correction.

The blood vessel shape constructing unit 14 includes a region dividing (rough extraction) unit, a structure analyzing unit, a region dividing (precise extraction) unit, an adhesion detection / separation unit, a region setting unit, and a shape building unit.

The region dividing unit executes adaptive vessel region division that performs region division based on an adaptive criterion provided for each blood vessel, and recursive vessel region division that repeatedly performs precise extraction or precise extraction from rough extraction.

In the region setting, labeling each blood vessel and lesion site, and referring to the anatomical template and setting the blood vessel region necessary for blood flow analysis by the computer is called automatic blood vessel region setting.

The blood vessel shape constructing unit 14 first performs rough extraction by temporarily calculating a threshold value (step S4-1-1-1) and binarizing the entire image (step S4-1-1-2). As the threshold value, a threshold value calculated by setting a predetermined reference from a histogram of luminance values of the entire image is used. However, when it is determined that the degree of detection of adhesion described later is high, the threshold value is tightened. A certain blood vessel shape can be obtained at the stage of binarization of the entire image, but the blood vessel extraction accuracy is rough because a unique threshold value is given to the entire image.

Next, the blood vessel shape constructing unit performs thinning (step S4-2-1). Thinning is a technique for acquiring the center line of a blood vessel. Although several methods are well known, in this embodiment, for example, the core of the blood vessel is extracted by facing the skin from the outer peripheral side of the blood vessel one by one to the center, and a spline curve or the like in the blood vessel running direction. To the center line.

Thereafter, the blood vessel shape constructing unit 14 graphs the blood vessel structure using the acquired blood vessel center line (step S4-2-2). That is, the end / branch point is specified in the blood vessel center line, and a graph showing the connection relationship between the node and the edge as shown in FIG. 11B is acquired. Hereinafter, each blood vessel portion corresponding to the center line between the end points and the branch points is referred to as a blood vessel element.

Next, image analysis is performed with reference to the travel direction of each side plotted in a graph (step S4-2-3). More specifically, a tomographic image perpendicular to the blood vessel traveling direction is identified at each point in the blood vessel traveling direction, and luminance value change characteristics in the boundary region inside and outside the blood vessel are obtained for each blood vessel element from the group of tomographic images between the nodes. To do. That is, an adaptive internal / external determination criterion is obtained for each vascular element. In this embodiment, the half value of the difference between the luminance value on the blood vessel center line and the background luminance value is used as the inside / outside determination criterion. By using the inside / outside determination criterion for each blood vessel element, binarization can be performed adaptively for each blood vessel in the subsequent extraction processing. In this structural analysis, the luminance value change characteristic may be acquired for each blood vessel element, or the luminance value change characteristic may be acquired for each minute region obtained by subdividing one blood vessel element.

Next, the blood vessel shape constructing unit 14 realizes an improvement in accuracy by performing region division for each blood vessel element and then performing region division again from thinning again. By repeating region division, the center line is specified in a location-dependent manner, thereby improving the accuracy of blood vessel region division. In rough extraction, one criterion is applied to the whole as described above, but in precise extraction, optimization is applied to each region even in one blood vessel by repeating precise extraction that adaptively applies the criterion for each blood vessel element. Applied standards. In this embodiment, the blood vessel region specified by rough extraction is used as a reference, and the difference from the blood vessel region obtained each time precise extraction is performed is quantified. Then, an end condition for precise extraction is given by determining the convergence of this difference. That is, when the difference exceeds the threshold value, the precise extraction is repeated, and when the difference falls below the threshold value, the precise extraction is terminated.

Next, the blood vessel shape constructing unit 14 detects and separates adhesions (step S4-3). As shown in FIG. 10, the term “adhesion” as used herein refers to a phenomenon that appears as if the blood vessels are adhered due to a lack of image accuracy, although the blood vessels are not originally adhered to each other. This phenomenon is hereinafter referred to as “adhesion” or “adhesion artifact”.

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 area 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 (step S4-3-1). Specifically, as shown in FIG. 11, the detection of the adhesion site is performed by graphing the blood vessel shape and depth-first search of the graph. That is, end points and branch points are detected and their connection relations are examined.

For example, in the graph shown in FIG. 11B processed based on the blood vessel shape shown in FIG. 11A, 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 cycle (loop), and this cycle is detected by searching the depth of the graph. 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. 11 (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. 11C, a closed circuit is formed 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 nodes and sides, and the node at the time point determined to be closed (No. 6 in the example shown in FIG. 11C) is regarded as an adhesion site. Can do.

When the blood vessel shape constructing unit 14 detects the adhesion site, it next separates the adhesion site (step 4-3-2). 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. 12, 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 adhesion part, when the cross-sectional area is plotted along the travel of the blood vessel, only the adhesion section as shown in the lower graph in 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 shown 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. 12B shows a cross section perpendicular to the blood vessel running direction in the adhesion section. Two black dots 51 indicate the estimated center line. Assuming that the cross section of the blood vessel is an ellipse, the two ellipses 52 are fitted to the blood vessel contour 53 of the adhesion section under the condition that the centers of the two ellipses 52 are known and are in contact. The two ellipses fitted by the dotted line in FIG. 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.

Next, the blood vessel shape constructing unit 14 performs labeling for each blood vessel (step S4-4-1). Blood vessel labeling is performed by identifying main blood vessels and tracking each blood vessel based on graph data. The computer automatically labels each blood vessel by collating it with an anatomical template indicating the anatomical position and orientation. At this time, a position suspected of being a vascular lesion is specified. A vascular lesion is an aneurysm or stenosis of a blood vessel, and the shape of the blood vessel changes locally. The type and site of the lesion are identified by analyzing the shape change in the blood vessel running direction for each blood vessel. For example, the cross-sectional area locally increases for an aneurysm, whereas the cross-sectional area decreases locally for a stenosis. The lesion site / type is specified according to the degree of change in the cross-sectional area.

Next, the blood vessel shape constructing unit 14 deletes unnecessary blood vessels for constructing a blood vessel shape for blood flow analysis by filtering (step S4-4-2). Unnecessary blood vessels are fine blood vessels and short blood vessels that are inappropriate for blood flow analysis. Since the diameter and length of the blood vessel are obtained by thinning and graphing, filtering is performed by matching with a prescribed threshold value. Thereafter, a region for blood flow analysis is automatically extracted according to the type and site of the lesion (step S4-4-3). Here, the determination is made with reference to a template prepared in advance.

Next, the blood vessel shape constructing unit 14 forms a blood vessel phase with polygons by a marching cube method or the like, and acquires blood vessel shape data for blood flow analysis together with appropriate smoothing (step S4-5). Here, not only the marching cues, but also a method of using a mathematical model of the blood vessel wall surface may be used.

Thereafter, the blood vessel shape constructing unit 14 outputs blood vessel shape data, lesion site / alcohol specific data, image suitability, and image correction data (step S4-6).

(From shape measurement to output (steps S5 to S7))
Next, operations of the shape measuring unit 15 and the quality evaluating unit 16 will be described with reference to FIG.

In this case, the input is (1) blood vessel shape data, (2) lesion site / type, (3) image suitability, (4) image correction degree, and output is (1) blood vessel shape data, (2) blood vessel Shape measurement data, (3) lesion site / type, (4) lesion shape measurement data, and (5) quality score.

The shape measuring unit 15 converts the polygonal blood vessel shape data ((1) blood vessel shape data input in step S5-0 and (2) lesion site / type in each of the labeled blood vessel / lesion regions. )) To measure the shape (step S5-1). For normal blood vessel sites, an equivalent diameter is calculated by constructing an orthogonal cross section based on the center line of the blood vessel, which is the basis for shape measurement. In this embodiment, in addition to basic values such as length, surface area, and volume, vascular strain calculated from a deviation from a perfect circle is measured. For the lesion shape, shape measurement according to the type is performed. For example, in the case of an aneurysm, a neck length, an aneurysm depth, an aspect ratio, a surface area, a volume, and the like are measured.

After the shape measurement, the quality evaluation unit 16 performs quality evaluation on the obtained blood vessel shape data (step S6). In this embodiment, the quality evaluation is a comprehensive evaluation value of blood vessel shape data that is scored for (1) image appropriateness, (2) image correction, and (3) shape construction accuracy. . (1) (2) is as described above. (3) The shape construction accuracy is the blood vessel wall identification accuracy in this embodiment. Although there is not one method for quantifying the blood vessel wall specifying accuracy, in this embodiment, the blood vessel shape data that has been polygonized is quantified by superimposing it on a medical image. That is, it is performed using the luminance gradient value near the blood vessel wall.

More specifically, as shown in FIG. 14A, a line segment Xi is formed in the direction orthogonal to the blood vessel surface with respect to the center of gravity of the micro surface (micro triangular element) of the polygonal blood vessel shape data. The brightness gradient is calculated along the minute. FIG. 14B shows a graph in which the horizontal axis is Xi and the vertical axis is the luminance value. As shown in the figure, the luminance value decreases from the inside of the blood vessel to the outside of the blood vessel. The sharper the degree of decrease, the clearer the contrast inside and outside the blood vessel, which means that the blood vessel shape construction accuracy is higher. Therefore, the quality evaluation unit 16 calculates the luminance gradient for each triangular element of the blood vessel shape data. FIG. 14C shows a histogram of the luminance gradient in each triangular element of one blood vessel element. In this embodiment, a histogram is created for each blood vessel element, but the present invention is not limited to this. Thereafter, the shape construction accuracy is quantified by setting a threshold value for the histogram. For example, as indicated by diagonal lines in FIG. 14 (c), it is determined that the luminance gradient is equal to or lower than the threshold value as a quality deterioration, and a ratio equal to or lower than the threshold value is calculated.

Thereafter, the quality evaluation unit 16 calculates the quality evaluation value X from the following equation using (1) image appropriateness (α), (2) image correction (β), and (3) shape construction accuracy (γ). .

Here, the proportional coefficients k _α , k _β , and k _γ are values that are standardized such that the maximum value of the quality evaluation value X is 1, and are individually assigned to each value.

Finally, the device 10 outputs blood vessel shape data, blood vessel shape measurement data, lesion site / type, lesion shape measurement data, quality score, and the like (step S7). The quality score may be the quality evaluation value X itself, or the quality evaluation value X may be classified into grades and expressed as characters. Further, it may be displayed superimposed on the blood vessel shape data.

In addition, the configuration of each part of the apparatus according to the present invention is not limited to the illustrated configuration example, and various modifications are possible as long as substantially the same operation is achieved.

For example, in the above-described embodiment, the image determination unit 12 determines the image suitability based on the amount of noise. However, the present invention is not limited to this, and for example, based on image analysis information acquired by other image analysis. The degree of image suitability may be determined. Furthermore, various noise amounts are not limited to those shown in the above embodiment, and other noise amounts may be quantified by image analysis.

In the above-described embodiment, the manufacturer and modality are determined based on the data added to the medical image. However, the present invention is not limited to this, and for example, it corresponds to the input medical image. In this case, image attribute information including the manufacturer and modality may be acquired from an imaging device that has captured a medical image via a network or a cable. In the embodiment described above, a database for machine learning is set up outside the hospital, but this is not a limitation. For example, a database is set up in the hospital and communicates with other facilities via a network. Database updates and machine learning may be performed.

In the above embodiment, the image standardization unit 13 acquires the image correction degree based on the size of the area of the correction location. However, the present invention is not limited to this. For example, the luminance change value at the correction location, etc. Other information indicating the degree of correction before and after correction may be acquired as the degree of image correction.

Further, for example, in the above embodiment, the difference between the blood vessel region obtained each time precise extraction is performed using the blood vessel shape construction unit 14 as a reference by the blood vessel region specified by rough extraction, and the convergence of the difference is determined. However, the method of giving the end condition is not limited to this, and other end conditions can be used as long as the quality of the blood vessel shape for blood flow analysis can be guaranteed. There may be. For example, the number of precision extractions may be included in the end condition.

In addition, the apparatus according to the embodiment includes an output unit, and outputs blood vessel shape data, blood vessel shape measurement data, a lesion site / type, lesion shape measurement data, and a quality score. May be transmitted wired or wirelessly and displayed on other devices such as other personal computers or laptops, smartphones, tablets, and the like.

In the present invention, in addition to the processing in each part of the device described in the above embodiment, the device in the present invention may add other processing suitable for constructing a precise blood vessel shape without user dependency. . In addition, it should be understood that the blood vessel shape constructing apparatus, the method thereof, and the computer program of the present invention can be applied to various uses as long as they have substantially the same operation.

Claims (32)

1. An input availability determination unit that receives a medical image, determines an image attribute of the medical image and an appropriateness as a medical image, and determines whether the medical image can be processed based on the attribute;
   The medical image determined to be processable as the input image is corrected / corrected based on the determined image attribute and appropriateness, the image quality of the medical image is standardized, and the degree of correction / correction is indicated. An image standardization unit for outputting the correction degree;
   A blood vessel shape constructing unit for constructing a three-dimensional blood vessel shape based on the standardized medical image;
   The blood vessel shape construction accuracy of the constructed three-dimensional blood vessel shape is determined, and a total quality evaluation of the constructed three-dimensional blood vessel shape is calculated and output based on the blood vessel shape construction accuracy, the appropriateness, and the correction degree. A blood vessel shape quality evaluation unit;
   A blood vessel shape data output unit that outputs the blood vessel shape data indicating the three-dimensional blood vessel shape together with the shape evaluation.
2. The apparatus of claim 1.
   The image attribute of the medical image is information including information on the manufacturer and modality of the imaging device that captured the medical image and imaging conditions.
   The appropriateness as the medical image is information including the noise level of the medical image,
   The input availability determination unit
   The determination as to whether or not input is possible is performed by referring to a predetermined template for image attributes and appropriateness.
3. The apparatus of claim 2.
   The apparatus according to claim 1, wherein the input availability determination unit is configured to determine a noise level by performing image analysis on a medical image.
4. The apparatus of claim 3.
   The input enable / disable determining unit calculates different types of noise components according to the image attributes by image analysis, and determines the noise level of each component based on the calculated noise components.
5. The apparatus of claim 1.
   The image standardization unit standardizes image quality by executing correction / correction to remove a specific noise component from the medical image based on the image attribute and the degree of appropriateness.
6. The apparatus of claim 1.
   The image standardization unit performs correction / correction to perform isotropicization as the image quality standardization by equalizing the spatial resolution in the X direction, the Y direction, and the Z direction by interpolation in an orthogonal XYZ coordinate system of a medical image A device characterized by that.
7. The apparatus of claim 1.
   The image standardization unit executes correction / correction for increasing the resolution as the image quality standardization by increasing the spatial resolution of the medical image by image interpolation.
8. The apparatus of claim 1.
   The image standardization unit executes correction of image distortion derived from imaging and correction of shape distortion of a local blood vessel shape as the standardization of the image quality.
9. The apparatus of claim 8.
   The image standardization unit performs correction of the shape distortion of the local blood vessel shape on the medical image determined as non-contrast MRA by the determination of the image attribute. The image standardization unit The distortion is corrected based on the luminance information of the region corresponding to the inside and the outside of the blood vessel on the medical image so as to eliminate the flow velocity dependence between the inside and the outside of the blood vessel, which is a characteristic of non-contrast MRA. A device characterized by what is to be performed.
10. The apparatus of claim 9.
    The image standardization unit detects a location where the flow velocity dependence of the medical image is stronger than a predetermined value based on luminance information of a location corresponding to the vicinity of a blood vessel wall on the medical image, and based on blood vessel morphology information or blood flow information The flow rate dependency of the detected part is corrected.
11. The apparatus of claim 10.
    The image standardization unit detects a location where a luminance gradient value in each cross section orthogonal to a blood vessel center line in the vicinity of the blood vessel wall is equal to or less than a threshold as a location having strong flow velocity dependency. .
12. The apparatus of claim 10.
    The image standardization unit corrects the flow velocity dependency of the image by applying the statistical model of the blood vessel at the detected location when the flow velocity dependency is corrected based on the morphological information of the blood vessel. A device characterized by being.
13. The apparatus of claim 12.
    The apparatus according to claim 1, wherein the statistical model of blood vessels is obtained by mathematically modeling a change in blood vessel cross section in a blood vessel traveling direction obtained from a blood vessel shape database.
14. The apparatus of claim 10.
    The image standardization unit corrects the flow velocity dependency of the image by applying a blood flow model of the blood vessel at the detected location when the flow velocity dependency is corrected based on the blood flow information of the blood vessel. The device characterized by being.
15. The apparatus according to claim 14, wherein the blood flow model of the blood vessel is obtained by analyzing actual blood flow information measured by a phase contrast MRI method or blood flow information obtained by analyzing a flow velocity-dependent spatial distribution. A device characterized in that
16. The apparatus of claim 1.
    The image standardization unit removes bone and / or calcification that is easily recognized as a blood vessel as a high-intensity signal from a medical image determined as a CTA (X-ray computed tomography image) by determining the image attribute. A device that performs correction / correction.
17. The apparatus of claim 16.
    The image standardization unit removes bone using anatomical features of bone, or removes bone using a composite statistical model of blood vessel. The statistical model of blood vessel is obtained from a blood vessel shape database. An apparatus characterized by mathematically modeling changes in blood vessel cross section in the obtained blood vessel traveling direction.
18. The apparatus of claim 16.
    The image standardization unit removes calcification based on luminance characteristics specific to calcification that are not present in bones or blood vessels.
19. The apparatus of claim 1.
    The blood vessel shape constructing unit
    A coarse division unit that roughly divides a blood vessel region by image analysis of the medical image;
    A structural analysis of the roughly divided blood vessel region, and a precise division unit that performs a precise division of the blood vessel region based on the result of the structural analysis;
    A detection unit that detects the presence or absence of adhesion of a specific blood vessel based on the result of the structural analysis,
    The blood vessel shape constructing unit re-executes the fine division of the blood vessel based on the determination of the presence or absence of the adhesion.
20. The apparatus of claim 1.
    The blood vessel shape constructing unit
    A coarse division unit that roughly divides a blood vessel region by image analysis of the medical image;
    A structural analysis of the roughly segmented blood vessel region, and a precise segmentation unit that performs precise segmentation of the blood vessel region based on the result of the structural analysis,
    The structural analysis acquires the centerline of the blood vessel,
    Once the center line of the blood vessel is obtained, an orthogonal image is acquired at each point on the center line, the luminance value analysis of the orthogonal image is performed on each blood vessel, and the blood vessel region-specific determination of inside / outside blood vessel is performed from the luminance value analysis,
    The precision dividing unit divides a blood vessel region based on the result of the blood vessel inside / outside determination.
21. The apparatus of claim 20.
    The inside / outside determination by the brightness value analysis is based on a half value of the maximum brightness value in the center line orthogonal image.
22. The apparatus of claim 20.
    The blood vessel shape constructing unit improves the accuracy of blood vessel region segmentation / extraction by recursively determining whether the blood vessel region is segmented / extracted in the blood vessel region segmentation / extraction.
23. A computer software program executed by a computer, each instruction stored in the following storage medium:
    An input availability determination unit that receives a medical image, determines an image attribute of the medical image and an appropriateness as a medical image, and determines whether the medical image can be processed based on them;
    The computer corrects / corrects the medical image determined to be processable as the input image based on the determined image attribute and appropriateness, standardizes the image quality of the medical image, and performs the correction / correction An image standardization unit that outputs a correction degree indicating the degree;
    A blood vessel shape constructing unit configured to construct a three-dimensional blood vessel shape based on the standardized medical image;
    The computer determines the blood vessel shape construction accuracy of the constructed three-dimensional blood vessel shape, and calculates the overall quality evaluation of the constructed three-dimensional blood vessel shape based on the blood vessel shape construction accuracy, the appropriateness, and the correction degree. A blood vessel shape quality evaluation unit that outputs
    A computer software program, comprising: a blood vessel shape data output unit that outputs blood vessel shape data indicating the three-dimensional blood vessel shape together with the shape evaluation.
24. 24. A computer software program according to claim 23.
    The image attribute of the medical image is information including information on the manufacturer and modality of the imaging device that captured the medical image and imaging conditions.
    The appropriateness as the medical image is information including the noise level of the medical image,
    The computer is a computer software program characterized in that the input enable / disable determining unit is configured such that a computer executes the determination of input enable / disable by referring to a predetermined template for image attributes and appropriateness.
25. 24. A computer software program according to claim 23.
    The image standardization unit is a computer software in which a computer performs correction of image distortion derived from imaging and correction of shape distortion of a local blood vessel shape to standardize the image quality. program.
26. 24. A computer software program according to claim 23.
    The blood vessel shape constructing unit
    A coarse division unit that roughly divides a blood vessel region by image analysis of the medical image;
    A structural analysis of the roughly divided blood vessel region, and a precise division unit that performs a precise division of the blood vessel region based on the result of the structural analysis;
    A detection unit that detects the presence or absence of adhesion of a specific blood vessel based on the result of the structural analysis,
    The blood vessel shape constructing unit is a computer software program in which a computer re-executes the precise division of the blood vessel based on the determination of the presence or absence of the adhesion.
27. 24. A computer software program according to claim 23.
    The blood vessel shape constructing unit
    A coarse division unit that roughly divides a blood vessel region by image analysis of the medical image;
    A structural analysis of the roughly segmented blood vessel region, and a precise segmentation unit that performs precise segmentation of the blood vessel region based on the result of the structural analysis,
    The structural analysis is
    Get the centerline of the blood vessel,
    Once the center line of the blood vessel is obtained, an orthogonal image is acquired at each point on the center line, the luminance value analysis of the orthogonal image is performed on each blood vessel, and the blood vessel site-specific determination of the inside and outside of the blood vessel is performed from the luminance value analysis.
    The computer software program, wherein the precision dividing unit divides a blood vessel region based on a result of the blood vessel inside / outside determination.
28. A method performed by a computer,
    A computer that receives a medical image, determines an image attribute of the medical image and a degree of appropriateness as a medical image, and determines whether or not it can be processed as an input image based on them;
    The computer corrects / corrects the medical image determined to be processable as the input image based on the determined image attribute and appropriateness, standardizes the image quality of the medical image, and performs the correction / correction An image standardization step for outputting a correction degree indicating the degree;
    A blood vessel shape constructing step in which a computer constructs a three-dimensional blood vessel shape based on the standardized medical image;
    The computer determines the blood vessel shape construction accuracy of the constructed three-dimensional blood vessel shape, and calculates the overall quality evaluation of the constructed three-dimensional blood vessel shape based on the blood vessel shape construction accuracy, the appropriateness, and the correction degree. And outputting blood vessel shape quality evaluation process,
    And a blood vessel shape data output step of outputting blood vessel shape data indicating the three-dimensional blood vessel shape together with the shape evaluation.
29. 30. The method of claim 28, wherein
    The image attribute of the medical image is information including information on the manufacturer and modality of the imaging device that captured the medical image and imaging conditions.
    The appropriateness as the medical image is information including the noise level of the medical image,
    In the method of determining whether or not input is possible, the computer executes the determination of whether or not input is possible by referring to a predetermined template with respect to image attributes and appropriateness.
30. 30. The method of claim 28, wherein
    In the image standardization step, a computer executes correction of image distortion derived from imaging and correction of local distortion of a blood vessel shape, and standardizes the image quality.
31. 30. The method of claim 28, wherein
    The blood vessel shape construction step includes
    A coarse division step of roughly dividing a blood vessel region by image analysis of the medical image;
    A precision division step of performing structural analysis of the roughly divided blood vessel region and executing fine division of the blood vessel region based on the result of the structural analysis;
    A detection step of detecting the presence or absence of adhesion of a specific blood vessel based on the result of the structural analysis,
    In this blood vessel shape constructing step, the computer re-executes the fine division of the blood vessel based on the determination of the presence or absence of the adhesion.
32. 30. The method of claim 28, wherein
    The blood vessel shape construction step includes
    A coarse division step of roughly dividing a blood vessel region by image analysis of the medical image;
    A structural analysis of the roughly segmented blood vessel region, and a precise segmentation step of performing a precise segmentation of the blood vessel region based on the result of the structural analysis,
    The structural analysis is
    Get the centerline of the blood vessel,
    Once the center line of the blood vessel is obtained, an orthogonal image is acquired at each point on the center line, the luminance value analysis of the orthogonal image is performed on each blood vessel, and the blood vessel site-specific determination of the inside and outside of the blood vessel is performed from the luminance value analysis.
    The precision segmentation step divides a blood vessel region based on the result of the inside / outside blood vessel determination.

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