JP2018102351A - Fibrous tissue extraction device, fibrous tissue extraction method, and fibrous tissue extraction program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a filamentous tissue extraction device, a filamentous tissue extraction method, and a filamentous tissue extraction program in which tracking results of forward direction tracking and reverse direction tracking coincide with each other in extracting a filamentous tissue using a diffusion weighted image taken by a magnetic resonance imaging apparatus.SOLUTION: A filamentous tissue extraction device includes: a start point setting part for setting a point on a diffusion weighted image to a starting point; an origin setting part for setting the starting point to an origin; a candidate point setting part for setting a plurality of candidate points on the origin; a candidate point selection part for selecting a candidate point; a candidate point determination part for determining one candidate point and a curve on the basis of an index of a curve connecting the origin and the candidate point; an origin update part for setting the determined one candidate point to a next origin; and a control part for repeating processing of the candidate setting part to the origin update part until there is no candidate point to be selected, and extracting the curve determined by the candidate point determination part as a filamentous tissue.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a method for extracting a fibrous tissue using a diffusion weighted image (hereinafter referred to as a DWI image) captured by a magnetic resonance imaging apparatus (hereinafter referred to as an MRI apparatus), an apparatus for extracting and displaying the fibrous tissue. The present invention also relates to a program for extracting fibrous tissue.

  The specimen is placed in a gradient magnetic field, irradiated with radio waves corresponding to the excitation energy of the magnetic moment of the hydrogen nucleus, and then the echo signal generated when the magnetic moment of the hydrogen nucleus returns to the ground state is detected, and the morphological image of the subject And an MRI apparatus that obtains movement information of biomolecules in a subject. The water molecules in the subject exhibit diffusion with anisotropy according to the structure in the subject by the action of a gradient magnetic field.

  In particular, it is known to diffuse with great anisotropy along the traveling direction of the fibrous tissue. As a method of extracting fibrous tissue, there is a method of tracking and extracting the path of fibrous tissue using a diffusion tensor calculated from DWI images obtained by applying multiple gradient magnetic fields with different application directions. . Conventionally, the next start point is set in the direction of the maximum eigenvector of the diffusion tensor from the set tracking start point, and the fibrous tissue is extracted by repeating this.

Japanese Patent Laid-Open No. 11-000320

  In the tracking method described in Patent Document 1, the process of setting the next start point based on only the direction of the maximum eigenvector of the start point is repeated to extract the fibrous tissue. If this process is repeated, errors will accumulate, and the extraction accuracy of the fibrous tissue may be reduced, or the extraction results of the forward tracking and the backward tracking may not match due to erroneous extraction.

  The present invention sets a plurality of candidate points to be tracked for one start point, generates a curve connecting the start point and the candidate point using both the maximum eigenvector of the start point and the maximum eigenvector of the candidate point, and generates Provided is a fibrous tissue extraction method for determining one candidate point out of a plurality of candidate points based on the index of the curve. Therefore, the fibrous tissue extraction device, the fibrous tissue extraction method, and the fibrous tissue extraction program provided by the present invention are tracking methods in which the extraction result in the forward direction and the extraction result in the reverse direction are in principle identical, It is possible to improve the extraction accuracy of the fibrous tissue while reducing the possibility of extraction.

  In order to solve the above-mentioned problem, the present invention is a fibrous tissue extraction apparatus that extracts a fibrous tissue using a DWI image captured by an MRI apparatus, and is a diffusion calculated at a point on the DWI image Diagonalizing the tensor and calculating the eigenvalue and the maximum eigenvector corresponding to the eigenvalue with the maximum absolute value, the starting point setting unit for setting the point on the DWI image as the starting point, and the start A starting point setting unit for setting a point as a starting point, and a plurality of points whose direction from the starting point is equal to or less than a predetermined angle with an angle formed with the direction of the maximum eigenvector at the starting point, and the distance from the starting point is within a predetermined range A candidate point setting unit that sets a candidate point having a diffusion anisotropy equal to or greater than a threshold value from among the plurality of candidate points, and a tangent vector at the starting point and the candidate point. Each maximum eigenvector A curve generating unit that generates a curve connecting the starting point and the candidate point for each of the plurality of candidate points so as to be in a direction, and one of the plurality of candidate points based on the generated index of the curve A candidate point, a candidate point determining unit for determining the starting point and one curve connecting the one candidate point, and a starting point for setting the one candidate point determined by the candidate point determining unit as a next starting point The candidate point setting unit, the candidate point selecting unit, the curve generating unit, the candidate point determining unit, and the starting point updating unit until there is no candidate point selected by the update unit and the candidate point selecting unit. A control unit that repeats the process and connects all the curves determined by the candidate point determination unit and extracts them as a fibrous tissue.

  The fibrous tissue extraction method according to the present invention is a fibrous tissue extraction method that extracts a fibrous tissue using a DWI image captured by an MRI apparatus, and is calculated at points on the DWI image. Diagonalization of the diffusion tensor, eigenvalue, diagonalization calculation step for calculating the maximum eigenvector corresponding to the eigenvalue having the maximum absolute value, a start point setting step for setting a point on the diffusion weighted image as a start point, A starting point setting step for setting the set starting point as a starting point, a direction from the set starting point being equal to or less than a predetermined angle with an angle formed by the direction of the maximum eigenvector at the starting point, and a distance from the starting point being within a predetermined range A candidate point setting step for setting a plurality of points as candidate points, a candidate point selecting step for selecting a candidate point having a diffusion anisotropy equal to or greater than a threshold value from the plurality of candidate points, and the starting point Based on a curve generation step for generating a curve connecting the starting point and the candidate point for each of the plurality of candidate points such that a tangent vector at the candidate point is in the direction of each maximum eigenvector, and the generated index of the curve A candidate point determining step for determining one candidate point and one curve connecting the starting point and the candidate point from among the plurality of candidate points; and determining the determined one candidate point as the next starting point The candidate point setting step, the candidate point selection step, the curve generation step, and the candidate point determination step until there is no candidate point selected by the candidate point selection step, And a control step of repeating all the starting point updating steps, connecting all the curves determined by the candidate point determining step, and extracting them as a fibrous tissue. And wherein the Rukoto.

  According to the present invention, a fibrous tissue extraction device and a fibrous tissue extraction method that are connected by a curve that touches the maximum eigenvector of each point so that the lines at the nodes are continuous and the possibility of erroneous extraction of the fibrous tissue is reduced. As well as a fibrous tissue extraction program.

The figure which shows the hardware constitutions of the fibrous tissue extraction apparatus which concerns on this invention The figure which shows the example of the display screen of the display apparatus which concerns on this invention Flow chart showing the flow of processing of the fibrous tissue extraction method in Example 1 Flow chart for explaining the details of the fibrous tissue extraction processing in Example 1 The figure which shows the relationship between the starting point and candidate point in Example 1 The figure for explaining selecting the candidate point which the maximum eigenvector of the diffusion tensor of each candidate point faces outside the straight line connecting each candidate point and the starting point from the candidate points Diagram for explaining how to connect the starting point and the candidate point with a curve Diagram for explaining how to calculate curvature Flow chart illustrating details of fibrous tissue extraction processing in Example 2 The figure which shows the relationship between the starting point and candidate point in Example 2

  Hereinafter, preferred embodiments of a fibrous tissue extraction device according to the present invention will be described with reference to the accompanying drawings. In the following description and the accompanying drawings, components having the same function are denoted by the same reference numerals, and repeated description thereof is omitted.

  FIG. 1 shows a hardware configuration of a fibrous tissue extraction device 1 according to the present invention. The fibrous tissue extraction device 1 includes a CPU (Central Processing Unit) 2, a main memory 3, a storage device 4, a display memory 5, a display device 6, a controller 7 connected to a mouse 8, a keyboard 9, and a network adapter 10, The bus 11 is configured so as to be able to send and receive signals.

  The fibrous tissue extraction apparatus 1 is connected to a magnetic resonance imaging apparatus (MRI apparatus) 13 and a medical image database 14 via a network 12 so as to be able to send and receive signals. Here, “to enable signal transmission / reception” indicates a state in which signals can be transmitted / received to each other or from one to the other, regardless of whether they are electrically or optically wired or wireless.

  The CPU 2 is a device that controls the operation of each component. The CPU 2 loads the program stored in the storage device 4 and data such as medical image information necessary for executing the program into the main memory 3 and executes them. The storage device 4 is, for example, a hard disk. The storage device 4 may be any device that exchanges data, and may be a portable recording medium such as a flexible disk, an optical (magnetic) disk, a ZIP memory, or a USB memory.

  The medical image information is acquired from the medical image database 14 and the MRI apparatus 13 via a network 12 such as a LAN (Local Area Network).

  The display memory 5 temporarily stores display data to be displayed on the display device 6 such as a liquid crystal display or a CRT (Cathode Ray Tube).

  The mouse 8 and the keyboard 9 are operation devices for an operator to give an operation instruction to the fibrous tissue extraction device 1. The mouse 8 may be another pointing device such as a trackpad or a trackball. The controller 7 detects the state of the mouse 8, acquires the position of the mouse pointer on the display device 6, and outputs the acquired position information and the like to the CPU 2.

  The network adapter 10 is for connecting the fibrous tissue extraction device 1 to a network 12 such as a LAN, a telephone line, or the Internet.

  The MRI apparatus 13 is an apparatus that captures a DWI image necessary for extracting a fibrous tissue. The medical image database 14 is a database system that stores medical image information captured by the MRI apparatus 13.

  When the CPU 2 executes a method described later, a fibrous tissue is extracted. The extracted fibrous tissue is displayed on the display device 6.

  FIG. 2 shows a display screen 200 of the display device 6 according to the present invention. The display screen 200 includes an image display unit 210, a start point region setting unit 220, an image operation unit 230, and a tracking start instruction unit 240. Using the start point region setting unit 220, the start point region 212 is set on the MPR image or 3D image 211 displayed on the image display unit 210. 213 and 214 superimposed on the MPR image or the 3D image 211 are fibrous tissues extracted by executing a tracking method described later, and the fibrous tissues 213 extracted by forward tracking are respectively inverted. A fibrous tissue 214 extracted by direction tracking is shown.

Hereinafter, the processing flow of the first embodiment will be described with reference to FIG.

(Step S301)
The storage device 4 reads the DWI image from the MRI apparatus 13 or the medical image database 14.

(Step S302)
CPU 2 calculates a diffusion tensor at each point on the DWI image read in step S301. The pixel value S of the DWI image obtained by applying the gradient magnetic field is given by the following (formula 1).

Here, S ₀ is a pixel value when no gradient magnetic field is applied, and b is a scalar amount determined by the gradient magnetic field strength, the gradient magnetic field application time by a pulse sequence, and the like, and is called a b value (b factor). G is a unit vector indicating the application direction of the gradient magnetic field, and D is a diffusion tensor. The diffusion tensor D is expressed as (Equation 2) using a 3 × 3 symmetric matrix.

  There are six unknowns of the diffusion tensor D, which can be obtained using a DWI image obtained by applying a gradient magnetic field G having six or more different application directions.

(Step S303)
The CPU 2 diagonalizes the diffusion tensor D calculated in S302 and calculates three eigenvalues and eigenvectors. Of the calculated eigenvectors, the eigenvector corresponding to the eigenvalue having the maximum absolute value is stored as the maximum eigenvector.

(Step S304)
While referring to the display screen 200, the operator operates the start point area setting unit 220 using the touch panel monitor when the mouse 8 and the keyboard 9 or the display device 6 is a touch panel monitor, and displays the image display unit 210. A start point area 212 is set on the displayed MPR image or 3D image 211. For example, the start point region 212 is set to a region where the fibrous tissue to be extracted is likely to pass. After setting the start point region 212, the operator operates the tracking start instruction unit 240, and the CPU 2 starts the fibrous tissue extraction process.

  Note that the above setting of the start point region is an example of a method for setting a start point for extracting a fibrous tissue. Instead of setting the start point region, a point on the MPR image or 3D image 211 displayed on the image display unit 210 may be pointed as a start point, and fibrous tissue may be extracted from the pointed point.

(Step S305)
The CPU 2 sets a plurality of points included in the start point region set in S304 as start points, and extracts a fibrous tissue in the forward direction based on the maximum eigenvector. Details of step S305 will be described below with reference to FIG.

(Step S306)
The CPU 2 multiplies the maximum eigenvector by −1 and extracts the fibrous tissue in the reverse direction by the same processing as S305.

(Step S307)
The CPU 2 superimposes the fibrous tissue extracted in S305 and S306 on the MPR image or 3D image 211, and displays it on the image display unit 210. Reference numeral 213 denotes a fibrous tissue extracted by the forward tracking of S305, and reference numeral 214 denotes a fibrous tissue extracted by the backward tracking of S306.

  FIG. 2 shows both the fibrous tissue 213 extracted by the tracking in the forward direction and the fibrous tissue 214 extracted by the tracking in the backward direction. One process may be omitted, and the fibrous tissue may be tracked only in either the forward direction or the reverse direction.

  Hereinafter, the details of the processing of S305 will be described with reference to FIGS. FIG. 4 is a flowchart for explaining the contents of the process of S305.

(Step S401)
The CPU 2 sets one point included in the start point area 212 as a start point, and starts extracting fibrous tissue for the set one start point.

(Step S402)
CPU2 sets the starting point as the starting point.

(Step S403)
The CPU 2 sets, as candidate points, a plurality of points whose direction from the starting point is equal to or less than a predetermined angle with respect to the direction of the maximum eigenvector at the starting point and whose distance from the starting point is within a predetermined range.

  FIG. 5 shows the relationship between the starting point and the candidate points. The CPU 2 selects a plurality of points included in the candidate point setting area 506 in the direction of a predetermined angle or less around the maximum eigenvector 502 at the starting point 501 and within a predetermined range from the starting point 501 as candidate points 503 (503 -1, 503-2, 503-3, 503-4).

(Step S404)
The CPU 2 selects candidate points showing fibrous anisotropic diffusion from the set candidate points.

The degree of anisotropic diffusion is evaluated using diffusion anisotropy. The diffusion anisotropy is a value indicating the degree of bias of diffusion, and the greater the diffusion anisotropy, the greater the fibrous anisotropic diffusion. The diffusion anisotropy is given by the following (formula 3) using eigenvalues λ ₁ , λ ₂ , and λ ₃ of the _three diffusion tensors.

Here, Dav is an apparent diffusion coefficient (Apparent Diffusion Coefficient), and is given by (Equation 4) below.

  The CPU 2 calculates the diffusion anisotropy of the above (Equation 3) and selects candidate points where the diffusion anisotropy is equal to or greater than a threshold value.

(Step S405)
The CPU 2 further determines a plane including a direction vector connecting the starting point 501 and the candidate point 503 from the selected candidate points 503, and has a maximum eigenvector extending in the opposite direction across the plane from the maximum eigenvector of the starting point 501. Candidate points 503 are selected.

  More specifically, CPU 2 selects candidate points whose maximum eigenvector of the diffusion tensor of each candidate point is directed outward from the straight line connecting each candidate point and starting point 501 from the candidate points selected in (2). To do. FIG. 6 is a diagram for explaining selection of candidate points whose maximum eigenvector of the diffusion tensor of each candidate point is directed outward from a straight line connecting each candidate point and the starting point 501. The reason for performing the selection processing in this step is that the outer side of the straight line 603 connecting the candidate point 503 and the starting point 501 as shown in FIG. 6, such as the maximum eigenvector 504 of the diffusion tensor of the candidate point 503 in the candidate point setting area 506 This is because the connection is smooth as shown by the curve 505, but if it faces the inside like the maximum eigenvector 604, it is connected by the S-shaped curve 605 and does not become a natural connection. .

  The ideal implementation method for this step is shown below.

  Determines the normal of the plane extending from the straight line 603 and the maximum eigenvector 502, determines the plane normal to the straight line 603 as the boundary plane, and determines the unit normal vector of the boundary plane in which the inner product of the maximum eigenvector 502 is positive To do. Further, the candidate point having the largest eigenvector whose inner product with the unit normal vector is negative is selected.

  Note that the process of step S405 is an optional process and may be implemented as necessary.

(Step S406)
From the selected candidate points, the CPU 2 further selects candidate points 503 whose angle between the maximum eigenvector 502 of the starting point 501 and the maximum eigenvector 504 of the candidate points is a predetermined value or less.

  Note that the process of step S406 is an optional process and may be implemented as necessary.

(Step S407)
CPU2 checks whether candidate points remain. If candidate points remain (Yes), the process proceeds to step S408, and one candidate point is determined from among a plurality of candidate points. If no candidate points remain (No), the process proceeds to step S411.

(Step S408)
For each selected candidate point, the CPU 2 generates a curve 505 connecting the starting point and the candidate point so that the starting point and the tangent vector at the candidate point are in the direction of the maximum eigenvector. FIG. 7 shows a conceptual diagram of a method of connecting a starting point and a candidate point with a curve. The curve 505 is preferably a cubic spline curve. Assuming that the three-dimensional coordinates of the starting point 501 are (x ₀ , y ₀ , z ₀ ), the cubic spline curve passing through the starting point 501 uses the parameter s as follows (Formula 5) and (Formula 6) expressed.

Here, {a _n , b _n , c _n | n = x, y, z} are unknown constants. In order to determine the maximum eigenvector 502 of the diffusion tensor at the starting point 501 and the cubic spline curve 505 in contact with the maximum eigenvector 504 of the diffusion tensor at the candidate point 503, nine conditional expressions are required. Three conditions from the condition that touches the maximum eigenvector 502 at the starting point 501, three from the condition that touches the maximum eigenvector 504 at the candidate point 503, and three conditions from the condition that passes through the coordinates (x ₁ , y ₁ , z ₁ ) of the candidate point 503 Are obtained respectively. By solving these conditional expressions, nine unknowns {a _n , b _n , c _n | n = x, y, z} are determined, and the curve 505 is determined.

(Step S409)
The CPU 2 calculates the curvature of each curve 505 connecting each candidate point 503 and the starting point 501 generated in S408. FIG. 8 is a diagram for explaining a method of calculating the curvature. A plane 802 is a contact plane at a point 801 on the curve represented by r (s). The vector e ₁ (s) is a tangent vector at the point 801 on the curve r (s). The vector e ₂ (s) is a normal vector at the point 801 on the curve r (s). A normal vector on the plane 802 is called a main normal vector. A vector e ₃ (s) passing through a point 801 on the curve r (s) and perpendicular to the plane 802 is called a normal vector. When the point 801 changes by Δs, if the angle formed by e ₁ (s) and e ₁ (s + Δs) is Δθ, the main curvature k (s) is expressed by the following (formula 7).

Further, when the point 801 changes by Δs and the angle formed by e ₃ (s) and e ₃ (s + Δs) is Δφ, the second curvature τ (s) called torsion is expressed by the following (Equation 8). Is defined as

CPU 2 calculates and compares the curvature of each curve 505 connecting each candidate point 503 and the starting point 501. The curvature to be compared may be the main curvature k (s), or an evaluation function f (k (s), τ (s)) generated from the main curvature k (s) and the second curvature τ (s) It may be. As an example of the evaluation function f (k (s), τ (s)), for example, f (k (s), τ (s)) = w ₁ * k (s) + w ₂ * τ (s) is used. . Here, w ₁ and w ₂ are parameters determined empirically. The average value of f (k (s), τ (s)) on the route from the starting point 501 to the candidate point 503 is expressed as in (Equation 9) below.

Where l (s _i ) is the curve length of the divided curve,

It is.

(Step S410)
The CPU 2 determines the candidate point with the smallest curvature, the candidate point and the starting point from the average value <f> of the curvatures of the plurality of curves 505 connecting the starting point 501 and each candidate point 503 calculated in S409. Determine one curve to connect. One determined curve is extracted, and the determined candidate point is set as the next starting point. Returning to step S403, the fibrous tissue extraction process is continued for the next set starting point.

In the above, the average value <f> of curvature is used as an index for selecting candidate points, but instead of the average value <f> of curvature, the curve from the start point to the candidate points is changed into a plurality of curves. divided, calculate the curvature f (s _i) for each divided curve, the candidate point maximum value _{f max = max {f (s} i)} is a minimum of f (s _i) as the next starting point, the One curve connecting the candidate point and the starting point may be determined.

  Thereafter, the process from step S403 to step S410 is repeated until the candidate point is eliminated in step S406, and the fibrous tissue extraction process is continued.

(Step S411)
The CPU 2 uses the current start point as the end point of the fibrous tissue, and completes the fibrous tissue extraction process for the current start point. All the curves determined so far in S410 are connected to obtain the fibrous tissue extraction result for the current starting point.

(Step S412)
The CPU 2 confirms whether the fibrous tissue extraction processing has been completed for all start points in the start point region 212. When the fibrous tissue extraction processing has been completed for all the start points (in the case of Yes), the process proceeds to step S306, and the fibrous tissue is extracted in the reverse direction. When the fibrous tissue extraction process has not been completed for all the start points (in the case of No), the process proceeds to step S413.

(Step S413)
The CPU 2 sets another point included in the start point area 212 as the next start point. Proceeding to step S402, fibrous tissue extraction processing is performed for the next starting point.

  In the first embodiment, candidate points can be selected in a spatially wide region, so that an optimal candidate point can be determined as the next starting point, and the fibrous tissue extraction accuracy can be improved.

  FIG. 9 shows a processing flow in the second embodiment. FIG. 10 is a diagram illustrating a relationship between a starting point and candidate points in the second embodiment.

  In Example 1, the curvature of the curve connecting the starting point and the candidate point is used as an index for selecting the candidate point. In Example 2, the length of the curve connecting the starting point and the candidate point is used for selecting the candidate point. Use as an indicator. Therefore, in step S903, a point on the sphere that is equidistant from the starting point is set as a candidate point. The processing different from the first embodiment is the processing in steps S903, S909, and S910. Since the processes in steps S901 to S902, S904 to S908, and S911 to S913 are the same as the processes in steps S401 to S402, S404 to S408, and S411 to S413 in the first embodiment, description thereof is omitted.

(Step S903)
The CPU 2 has a point 503 (503-1, 502-2, 503- on the spherical surface 1006 that is equal to or less than a predetermined angle with respect to the direction of the maximum eigenvector at the starting point 501, and is equal to or less than a predetermined angle. 3, 503-4) are set as candidate points.

(Step S909)
The CPU 2 calculates the length of each curve r (s) connecting the starting point 501 generated in S908 and each candidate point 503. The length L of the curve is calculated using the following (formula 11).

(Step S910)
CPU2 determines a candidate point having the shortest length and a single curve connecting the candidate point and the starting point from among a plurality of curves 505 connecting the starting point 501 calculated in S909 and each candidate point 503. The determined candidate point is set as the next starting point. Returning to step S903, the fibrous tissue is extracted from the next starting point.

  In the second embodiment, a fibrous tissue can be extracted at a calculation cost lower than that in the first embodiment.

  The fibrous tissue extraction apparatus of the present invention is not limited to the above embodiment, and can be embodied by modifying the constituent elements without departing from the gist of the invention. For example, in the step of S408, a cubic spline curve is given as an example of a curve connecting a starting point and a candidate point, and the embodiment of the present invention has been specifically described. However, a starting point using a Bezier curve instead of a spline curve is described. And the candidate points may be connected, and the candidate points may be determined from the indices of the connected curves. Moreover, you may combine suitably the some component currently disclosed by the said embodiment. Furthermore, some components may be deleted from all the components shown in the above embodiment.

  1 Fibrous tissue extractor, 2 CPU, 3 main memory, 4 storage device, 5 display memory, 6 display device, 7 controller, 8 mouse, 9 keyboard, 10 network adapter, 11 system bus, 12 network, 13 magnetic resonance image Imaging device (MRI device), 14 medical image database, 200 display screen, 210 image display unit, 211 MPR image or 3D image, 212 starting point region, 213 fibrous tissue extracted by forward tracking, 214 reverse direction Fibrous tissue extracted by tracking, 220 start point area setting unit, 230 image operation unit, 240 tracking start instruction unit, 501 starting point, 502 maximum eigenvector, 503, 503-1, 502-2, 503-3, 503- 4 Candidate points, 504 maximum eigenvectors, 505 curves, 506 candidate point setting area, 603 straight lines, 604 maximum eigenvectors, 605 curves, 801 points, 802 plane, 1006 sphere

Claims (15)

A fibrous tissue extraction device that extracts a fibrous tissue using a diffusion weighted image captured by a magnetic resonance imaging apparatus,
Diagonalizing a diffusion tensor calculated at a point on the diffusion-weighted image, and calculating a eigenvalue and a maximum eigenvector corresponding to the eigenvalue having the maximum absolute value,
A starting point setting unit for setting a point on the diffusion weighted image as a starting point;
A starting point setting unit for setting the starting point as a starting point;
A candidate point setting unit configured to set a plurality of points as candidate points, wherein a direction from the starting point is equal to or less than a predetermined angle with an angle formed with the direction of the maximum eigenvector at the starting point, and a distance from the starting point is within a predetermined range; ,
A candidate point selection unit for selecting a candidate point having a diffusion anisotropy equal to or greater than a threshold value from the plurality of candidate points;
A curve generator for generating a curve connecting the starting point and the candidate point for each of the plurality of candidate points such that a tangent vector at the starting point and the candidate point is in the direction of each maximum eigenvector;
A candidate point determination unit for determining one candidate point and one curve connecting the starting point and the one candidate point from among the plurality of candidate points based on the index of the curve;
A starting point updating unit for setting the one candidate point determined by the candidate point determining unit as a next starting point;
Until there are no candidate points to be selected by the candidate point selection unit, the candidate point setting unit, the candidate point selection unit, the curve generation unit, the candidate point determination unit, and the starting point update unit are repeated, A control unit that connects all the curves determined by the candidate point determination unit and extracts as a fibrous tissue,
A fibrous tissue extraction apparatus comprising:   The fibrous tissue is extracted in both directions from the start point using both the maximum eigenvector calculated by the diagonalization calculation unit and the maximum eigenvector obtained by multiplying the maximum eigenvector by -1. 2. The fibrous tissue extraction device according to 1. A start point region setting unit for setting a predetermined region on the diffusion weighted image as a start point region;
The start point setting unit sets a start point one by one from a plurality of points included in the start point region,
The control unit controls to extract the fibrous tissue for all of a plurality of points included in the start point region;
The fibrous tissue extraction device according to claim 1 or 2, wherein the device is a fibrous tissue extraction device. The candidate point selection unit determines a plane including a direction vector connecting the starting point and the candidate point, and further selects the candidate point having the maximum eigenvector of the starting point and the maximum eigenvector extending in the opposite direction across the plane. ,
The fibrous tissue extraction device according to any one of claims 1 to 3, wherein The candidate point selection unit further selects a candidate point having an angle between a maximum eigenvector of the starting point and a maximum eigenvector of the candidate point that is equal to or less than a predetermined value from the plurality of candidate points.
The fibrous tissue extraction device according to any one of claims 1 to 4, wherein The indicator of the curve is an average principal curvature in the path of the curve,
The candidate point determination unit determines, from among the plurality of candidate points, one candidate point with the smallest index of the curve, and one curve connecting the starting point and the one candidate point;
The fibrous tissue extraction device according to any one of claims 1 to 5, wherein The index of the curve is an average value in the path of the curve of the value obtained by the evaluation function relating to the main curvature and the second curvature of the curve,
The candidate point determination unit determines, from among the plurality of candidate points, one candidate point with the smallest index of the curve, and one curve connecting the starting point and the one candidate point;
The fibrous tissue extraction device according to any one of claims 1 to 5, wherein The index of the curve is a maximum value of the main curvature calculated for each of the divided curves by dividing the curve into a plurality of curves.
The candidate point determination unit determines, from among the plurality of candidate points, one candidate point with the smallest index of the curve, and one curve connecting the starting point and the one candidate point;
The fibrous tissue extraction device according to any one of claims 1 to 5, wherein The index of the curve is a maximum value obtained by dividing the curve into a plurality of curves and obtained by an evaluation function relating to the main curvature and the second curvature calculated for each of the divided curves,
The candidate point determination unit determines, from among the plurality of candidate points, one candidate point with the smallest index of the curve, and one curve connecting the starting point and the one candidate point;
The fibrous tissue extraction device according to any one of claims 1 to 5, wherein The index of the curve is a length of a curve connecting the starting point and the candidate point,
The candidate point determination unit determines, from among the plurality of candidate points, one candidate point with the smallest index of the curve, and one curve connecting the starting point and the one candidate point;
The fibrous tissue extraction device according to any one of claims 1 to 5, wherein The curve connecting the starting point and the candidate point is a cubic spline curve.
The fibrous tissue extraction device according to any one of claims 1 to 10, wherein A fibrous tissue extraction method for extracting a fibrous tissue using a diffusion weighted image captured by a magnetic resonance imaging apparatus,
Diagonalizing a diffusion tensor calculated at a point on the diffusion-weighted image to calculate a eigenvalue and a maximum eigenvector corresponding to the eigenvalue having the maximum absolute value;
A starting point setting step of setting a point on the diffusion weighted image as a starting point;
A starting point setting step for setting the set starting point as a starting point;
Candidate point setting for setting a plurality of points as candidate points, the direction from the set starting point being equal to or less than a predetermined angle with the angle formed by the direction of the maximum eigenvector at the starting point, and the distance from the starting point being within a predetermined range Steps,
A candidate point selection step of selecting candidate points having a diffusion anisotropy equal to or greater than a threshold value from the plurality of candidate points;
A curve generating step for generating a curve connecting the starting point and the candidate point for each of the plurality of candidate points such that a tangent vector at the starting point and the candidate point is in the direction of each maximum eigenvector;
A candidate point determining step for determining one candidate point and one curve connecting the starting point and the candidate point from among the plurality of candidate points based on the generated index of the curve;
A starting point update step of setting the determined one candidate point as a next starting point;
The candidate point setting step, the candidate point selection step, the curve generation step, the candidate point determination step, and the starting point update step are repeated until there are no candidate points to be selected by the candidate point selection step. A control step of connecting all the curves determined by the candidate point determination step and extracting as a fibrous tissue;
A fibrous tissue extraction method comprising: A start point region setting step of setting a predetermined region on the diffusion weighted image as a start point region;
The start point setting step sets a start point one by one from a plurality of points included in the start point region,
The control step controls to extract the fibrous tissue for all of a plurality of points included in the start point region.
The fibrous tissue extraction method according to claim 12, wherein: A fibrous tissue extraction program for extracting fibrous tissue using a diffusion weighted image captured by a magnetic resonance imaging apparatus,
Diagonalizing a diffusion tensor calculated at a point on the diffusion-weighted image to calculate a eigenvalue and a maximum eigenvector corresponding to the eigenvalue having the maximum absolute value;
A starting point setting step of setting a point on the diffusion weighted image as a starting point;
A starting point setting step for setting the set starting point as a starting point;
Candidate point setting for setting a plurality of points as candidate points, the direction from the set starting point being equal to or less than a predetermined angle with the angle formed by the direction of the maximum eigenvector at the starting point, and the distance from the starting point being within a predetermined range Steps,
A candidate point selection step of selecting candidate points having a diffusion anisotropy equal to or greater than a threshold value from the plurality of candidate points;
A curve generating step for generating a curve connecting the starting point and the candidate point for each of the plurality of candidate points such that a tangent vector at the starting point and the candidate point is in the direction of each maximum eigenvector;
A candidate point determining step for determining one candidate point and one curve connecting the starting point and the candidate point from among the plurality of candidate points based on the generated index of the curve;
A starting point update step of setting the determined one candidate point as a next starting point;
The candidate point setting step, the candidate point selection step, the curve generation step, the candidate point determination step, and the starting point update step are repeated until there are no candidate points to be selected by the candidate point selection step. A control step of connecting all the curves determined by the candidate point determination step and extracting as a fibrous tissue;
A fibrous tissue extraction program comprising: A start point region setting step of setting a predetermined region on the diffusion weighted image as a start point region;
The start point setting step sets a start point one by one from a plurality of points included in the start point region,
The control step controls to extract the fibrous tissue for all of a plurality of points included in the start point region.
The fibrous tissue extraction program according to claim 14, wherein:

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