Brain image multi-index feature consistency analysis method and system

Abstract

The invention relates to the technical field of medical image processing, in particular to a brain image multi-index feature consistency analysis method and system. Converting the feature image of each brain image index to be analyzed into mask; according to the feature mask of each to-be-analyzed brain image index, constructing a linear function and calculating a result; and performing color mapping on the result, and performing superposition display with a brain map background. According to the method, different situations of brain image multi-index feature consistency are quantified into different numerical values in a function construction mode, so that the different situations are distinguished and are finally presented in a superposed effect picture form, and the technical problem of multi-index feature consistency analysis under the condition that the number of brain image indexes is 2 and 3 is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular, to a method and system for analyzing the consistency of multi-index features of brain images. Background Art

[0002] Exploring the consistency of different brain image index features is a problem often encountered in clinical and research work of neuroimaging, and it often has important clinical value or scientific significance. For example, ① in CT perfusion imaging of stroke patients, "rCBF < 30% of the contralateral normal brain tissue" represents the infarct core area, and "Tmax > 6 s" represents the ischemic hypoperfusion area. The area where the two are inconsistent is called mismatch, and the presence of mismatch is an indication for arterial thrombectomy and recanalization; ② in a case-control study combining arterial spin labeling (ASL) and resting-state functional magnetic resonance imaging (rs-fMRI) to explore changes in neurovascular coupling, the consistency of the inter-group differences in CBF, ALFF, and CBF / ALFF ratios can indicate the driving factors of neurovascular decoupling.

[0003] In this context, a method for analyzing the consistency of multi-index features of brain images is proposed. The invention is simple and easy to implement, and is expected to help physicians quickly analyze the consistency of multi-index features of brain images. By quantifying different situations of the consistency of multi-index features of brain images into different values, they can be distinguished, and finally presented in the form of a superimposed effect diagram; it can solve the problem of analyzing the consistency of multi-index features in the case of 2 and 3 brain image indexes, and provide a basis for the further popularization and promotion of relevant clinical and research work. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0005] To solve the above technical problems, the present invention provides the following technical solution. A method for analyzing the consistency of multi-index features of brain images includes: converting the feature image of each brain image index to be analyzed into a mask;

[0006] Constructing a linear function and calculating the result according to the feature mask of each brain image index to be analyzed;

[0007] Performing color mapping on the result and superimposing and displaying it on the brain map background.

[0008] As a preferred scheme of a method for analyzing the consistency of multi-index features of brain images according to the present invention, wherein: the feature image is an image used to determine whether an element is a feature, which is the index map itself at the individual level and the statistical parameter map at the group level.

[0009] As a preferred solution of a method for analyzing the consistency of multi-index features of brain images according to the present invention, wherein: the conversion of the feature image of each brain image index to be analyzed into a mask includes setting the feature image of the nth brain image index as I n , I n is a three-dimensional matrix of a×b×c, and a, b, and c respectively represent the number of elements of I n in the X, Y, and Z directions;

[0010] Let the feature of the nth brain image index be F n , F n is a three-dimensional matrix of a×b×c, and the values are composed of 0 and 1. Let the region satisfying the feature condition be the set S n , then the calculation method of F n is:

[0011]

[0012] wherein, I n (x,y,z) represents the element value at the position of X = x, Y = y, Z = z in the matrix I n , and F n (x,y,z) represents the element value at the position of X = x, Y = y, Z = z in the matrix F n .

[0013] As a preferred solution of a method for analyzing the consistency of multi-index features of brain images according to the present invention, wherein: the construction of the linear function and the calculation of the result include,

[0014] Let the output variable of the linear function be G, and G is a three-dimensional matrix of a×b×c. When the number of brain image indexes to be analyzed is 2, the linear function is constructed as:

[0015] G(x,y,z) = F 1 (x,y,z) + 2×F 2 (x,y,z)

[0016] wherein, F n (x,y,z) represents the element value at the position of X = x, Y = y, Z = z in the matrix F n , n = 1 or 2, and G(x,y,z) represents the element value at the position of X = x, Y = y, Z = z in the matrix G. When and only when F 1 (x,y,z) = 0 and F 2 (x,y,z) = 0, G(x,y,z) = 0; when and only when F 1 (x,y,z) = 1 and F 2 (x,y,z) = 0, G(x,y,z) = 1; when and only when F 1(x, y, z) = 0 and F 2 When (x, y, z) = 1, G(x, y, z) = 2; if and only if F 1 (x, y, z) = 1 and F 2 When (x, y, z) = 1, G(x, y, z) = 3; the area where the value of matrix G is 0 means that this area does not satisfy Feature 1 or Feature 2; the area where the value of matrix G is 1 means that this area satisfies Feature 1 but does not satisfy Feature 2; the area where the value of matrix G is 2 means that this area satisfies Feature 2 but does not satisfy Feature 1; the area where the value of matrix G is 3 means that this area satisfies both Feature 1 and Feature 2.

[0017] As a preferred solution of the multi-index feature consistency analysis method for brain images described in the present invention, wherein: the construction of the linear function and the calculation of the result further include,

[0018] When the number of brain image indicators to be analyzed is 3, the linear function is constructed as:

[0019] G(x, y, z) = F 1 (x, y, z) + 2×F 2 (x, y, z) + 4×F 3 (x, y, z)

[0020] Wherein, F n (x, y, z) represents the element value at the position X = x, Y = y, Z = z in matrix F n n = 1 or 2 or 3, G(x, y, z) represents the element value at the position X = x, Y = y, Z = z in matrix G, if and only if F 1 (x, y, z) = 0 and F 2 (x, y, z) = 0 and F 3 When (x, y, z) = 0, G(x, y, z) = 0; if and only if F 1 (x, y, z) = 1 and F 2 (x, y, z) = 0 and F 3 When (x, y, z) = 0, G(x, y, z) = 1; if and only if F 1 (x, y, z) = 0 and F 2 (x, y, z) = 1 and F 3 When (x, y, z) = 0, G(x, y, z) = 2; if and only if F 1 (x, y, z) = 1 and F 2 (x, y, z) = 1 and F 3 When (x, y, z) = 0, G(x, y, z) = 3; if and only if F 1 (x, y, z) = 0 and F 2 (x, y, z) = 0 and F 3When (x, y, z) = 1, G(x, y, z) = 4; if and only if F 1 (x, y, z) = 1 and F 2 (x, y, z) = 0 and F 3 When (x, y, z) = 1, G(x, y, z) = 5; if and only if F 1 (x, y, z) = 0 and F 2 (x, y, z) = 1 and F 3 When (x, y, z) = 1, G(x, y, z) = 6; if and only if F 1 (x, y, z) = 1 and F 2 (x, y, z) = 1 and F 3 When (x, y, z) = 1, G(x, y, z) = 7; the area where the matrix G value is 0 represents that this area does not satisfy Feature 1, Feature 2 or Feature 3; the area where the matrix G value is 1 represents that this area satisfies Feature 1 but does not satisfy Feature 2 and Feature 3; the area where the matrix G value is 2 represents that this area satisfies Feature 2 but does not satisfy Feature 1 and Feature 3; the area where the matrix G value is 3 represents that this area satisfies both Feature 1 and Feature 2 but does not satisfy Feature 3; the area where the matrix G value is 4 represents that this area satisfies Feature 3 but does not satisfy Feature 1 and Feature 2; the area where the matrix G value is 5 represents that this area satisfies both Feature 1 and Feature 3 but does not satisfy Feature 2; the area where the matrix G value is 6 represents that this area satisfies both Feature 2 and Feature 3 but does not satisfy Feature 1; the area where the matrix G value is 7 represents that this area satisfies Feature 1, Feature 2 and Feature 3.

[0021] As a preferred scheme of a method for analyzing the consistency of multi-index features of brain images according to the present invention, wherein: the step of performing color mapping on the results and superimposing them on the brain map background includes using a color bar to map non-zero values of the matrix G to different colors to distinguish different situations of consistency, as an overlay layer; selecting a brain map in the same space as the background layer; and superimposing the overlay layer on the background layer for display.

[0022] As a preferred scheme of a system for analyzing the consistency of multi-index features of brain images according to the present invention, wherein: it includes a feature sorting module, a function building and result calculating module, and a consistency visualization module;

[0023] The feature sorting module converts the feature image of each brain image index to be analyzed into a mask;

[0024] The function building and result calculating module builds a linear function and calculates the result according to the feature mask of each brain image index to be analyzed.

[0025] The consistency visualization module performs color mapping on the results and superimposes them on the brain map background for display.

[0026] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of any one of the methods in a method for analyzing the consistency of multi-index features of brain images are implemented.

[0027] A computer-readable storage medium stores a computer program thereon. It is characterized in that when the computer program is executed by a processor, the steps of any one of the methods in a method for analyzing the consistency of multi-index features of brain images are implemented.

[0028] Advantages of the present invention: By constructing a function, different situations of the consistency of multi-index features of brain images are quantified into different numerical values, so that these different situations can be distinguished and finally presented in the form of a superimposed effect diagram, solving the technical problem of analyzing the consistency of multi-index features in the case where the number of brain image indexes is 2 and 3. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0030] Figure 1 It is a flowchart of a method for analyzing the consistency of multi-index features of brain images according to the present invention;

[0031] Figure 2 It is the feature mask F of the first brain image index (CBF) obtained in step 1 of the embodiment of the present invention 1 The figure shows a total of 18 cross-sectional views (3 rows and 6 columns). The black part of each cross-sectional view represents a mask value of 0, the gray part represents a mask value of 1, and the white number in the lower left corner of each cross-sectional view represents the Z-axis coordinate of the corresponding cross-section. L and R at the top of the figure represent the left and right sides respectively.

[0032] Figure 3 It is the feature mask F of the second brain image index (ALFF) obtained in step 1 of the embodiment of the present invention 2 The figure is shown.

[0033] Figure 4 It is the feature mask F of the third brain image index (CBF / ALFF ratio) obtained in step 1 of the embodiment of the present invention 3 The figure is shown.

[0034] Figure 5This is the visualization effect diagram of consistency obtained in step 3 of the embodiment of the present invention. The background layer is the Ch2 template, and the overlay layer is the color mapping of the non-zero values of the matrix G obtained in step 2. The caption at the bottom of the figure represents the corresponding colors used to show the consistency of different index features. The caption text "CBF / ALFF ratio" represents the brain regions where only the CBF / ALFF ratio has significant inter-group differences, "CBF" represents the brain regions where only CBF has significant inter-group differences, "ALFF" represents the brain regions where only ALFF has significant inter-group differences, "CBF / ALFF ratio+CBF" represents the brain regions where the significant inter-group differences of the CBF / ALFF ratio and CBF overlap, and "CBF / ALFF ratio+ALFF" represents the brain regions where the significant inter-group differences of the CBF / ALFF ratio and ALFF overlap. Detailed implementation manners

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1

[0037] Refer to Figures 1 - 5 , which is the first embodiment of the present invention. This embodiment provides a method for analyzing the consistency of multi-index features of brain images. As Figure 1 shown, it includes:

[0038] Step 1, organize features; the specific process is as follows:

[0039] Convert the feature image of each brain image index to be analyzed into a mask. The "feature image" refers to an image that can be used to determine whether an element is a feature. At the individual level, it is the index map itself, and at the group level, it is the statistical parameter map. The algorithm is: for the feature image of each brain image index to be analyzed, traverse each element and determine whether the value of the element meets the conditions for forming a feature. If so, mark the position of the element as 1, otherwise mark it as 0. [Note: Mask is an important concept in the field of image processing, used to specify a certain area of an image, usually a binary image of the same size as the original image, where the selected area is marked as 1 and the rest of the area is marked as 0.]

[0040] Specifically, this embodiment is a case-control study based on arterial spin labeling (ASL) and resting-state functional magnetic resonance imaging (rs-fMRI), aiming to explore the consistency of inter-group differences in CBF, ALFF, and the CBF / ALFF ratio. Therefore, there are a total of 3 brain imaging indexes to be analyzed, namely CBF, ALFF, and the CBF / ALFF ratio; the characteristic images are statistical parametric maps at the group level, a total of 3, namely the T-map of CBF, the T-map of ALFF, and the T-map of the CBF / ALFF ratio. This step requires converting all 3 characteristic images (the T-map of CBF, the T-map of ALFF, and the T-map of the CBF / ALFF ratio) into masks, and the process is as follows:

[0041] Let the characteristic image of the first brain imaging index (CBF) (i.e., the T-map of CBF) be I 1 , I 1 be a three-dimensional matrix of 61×73×61, and 61, 73, and 61 represent the number of elements of I 1 in the X, Y, and Z directions respectively. Let the characteristic of the first brain imaging index be F 1 , F 1 be a three-dimensional matrix of 61×73×61, and the values are composed of 0 and 1. Let the region that meets the characteristic conditions be the set S 1 , and in this embodiment, the determination method of S 1 is: the region where the absolute value of I 1 is greater than or equal to the absolute value of the CBF T-value threshold. Then the calculation method of F1 is:

[0042]

[0043] where I 1 (x,y,z) represents the element value at the position of X = x, Y = y, Z = z in the matrix I 1 , and F 1 (x,y,z) represents the element value at the position of X = x, Y = y, Z = z in the matrix F 1 .

[0044] After the above processing in this embodiment, the characteristic mask of the first brain imaging index (CBF), that is, F 1 is obtained as shown Figure 2 .

[0045] Let the characteristic image of the second brain imaging index (ALFF) (i.e., the T-map of ALFF) be I 2 , I 2 be a three-dimensional matrix of 61×73×61, and 61, 73, and 61 represent the number of elements of I 2 in the X, Y, and Z directions respectively. Let the characteristic of the second brain imaging index be F 2 , F 2It is a three-dimensional matrix of 61×73×61, and the values are composed of 0 and 1. Let the region satisfying the characteristic condition be the set S 2 , in this embodiment, S 2 is determined as follows: I 2 The region where the absolute value ≥ the absolute value of the ALFF T-value threshold. Then F 2 is calculated as follows:

[0046]

[0047] where, I 2 (x, y, z) represents the element value at the position of X = x, Y = y, Z = z in the matrix I 2 , and F 2 (x, y, z) represents the element value at the position of X = x, Y = y, Z = z in the matrix F 2 .

[0048] After the above processing in this embodiment, the characteristic mask of the second brain imaging index (ALFF), that is, F 2 is as Figure 3 shown.

[0049] Let the characteristic image of the third brain imaging index (CBF / ALFF ratio) (i.e., the T-map of the CBF / ALFF ratio) be I 3 , and I 3 is a three-dimensional matrix of 61×73×61, and 61, 73, 61 respectively represent the number of elements of I 3 in the X, Y, and Z directions. Let the characteristic of the third brain imaging index be F 3 , and F 3 is a three-dimensional matrix of 61×73×61, and the values are composed of 0 and 1. Let the region satisfying the characteristic condition be the set S 3 , in this embodiment, S 3 is determined as follows: I 3 The region where the absolute value ≥ the absolute value of the CBF / ALFF ratio T-value threshold. Then F 3 is calculated as follows:

[0050]

[0051] where, I 3 (x, y, z) represents the element value at the position of X = x, Y = y, Z = z in the matrix I 3 , and F 3 (x, y, z) represents the element value at the position of X = x, Y = y, Z = z in the matrix F 3 .

[0052] After the above processing of this embodiment, the characteristic mask of the third brain imaging index (CBF / ALFF ratio), namely F, is obtained. 3 As Figure 4 shown.

[0053] Step 2: Construct a function and calculate the result, specifically:

[0054] According to the characteristic mask of each brain imaging index to be analyzed obtained in Step 1, construct the following linear function and calculate the result.

[0055] Specifically, let the output variable of the linear function be G, and G is a three-dimensional matrix of 61×73×61. Since the number of brain imaging indexes to be analyzed in this embodiment is 3, the linear function is constructed as:

[0056] G(x,y,z) = F 1 (x,y,z) + 2×F 2 (x,y,z) + 4×F 3 (x,y,z)

[0057] where F n (x,y,z) represents the element value at the position X = x, Y = y, Z = z in the matrix F n , and n = 1 or 2 or 3. G(x,y,z) represents the element value at the position X = x, Y = y, Z = z in the matrix G. Thus, it can be seen that when and only when F 1 (x,y,z) = 0 and F 2 (x,y,z) = 0 and F 3 (x,y,z) = 0, G(x,y,z) = 0; when and only when F 1 (x,y,z) = 1 and F 2 (x,y,z) = 0 and F 3 (x,y,z) = 0, G(x,y,z) = 1; when and only when F 1 (x,y,z) = 0 and F 2 (x,y,z) = 1 and F 3 (x,y,z) = 0, G(x,y,z) = 2; when and only when F 1 (x,y,z) = 1 and F 2 (x,y,z) = 1 and F 3 (x,y,z) = 0, G(x,y,z) = 3; when and only when F 1 (x,y,z) = 0 and F 2 (x,y,z) = 0 and F 3 (x,y,z) = 1, G(x,y,z) = 4; when and only when F 1 (x,y,z) = 1 and F 2 (x,y,z) = 0 and F3 When (x, y, z) = 1, G(x, y, z) = 5; if and only if F 1 (x, y, z) = 0 and F 2 (x, y, z) = 1 and F 3 When (x, y, z) = 1, G(x, y, z) = 6; if and only if F 1 (x, y, z) = 1 and F 2 (x, y, z) = 1 and F 3 When (x, y, z) = 1, G(x, y, z) = 7. Therefore, the region where the matrix G value is 0 represents that there are no significant differences in CBF, ALFF, and the CBF / ALFF ratio between groups in this region; the region where the matrix G value is 1 represents that there is a significant difference in CBF between groups in this region, but there are no significant differences in ALFF and the CBF / ALFF ratio between groups; the region where the matrix G value is 2 represents that there is a significant difference in ALFF between groups in this region, but there are no significant differences in CBF and the CBF / ALFF ratio between groups; the region where the matrix G value is 3 represents that there are significant differences in both CBF and ALFF between groups in this region, but there is no significant difference in the CBF / ALFF ratio between groups; the region where the matrix G value is 4 represents that there is a significant difference in the CBF / ALFF ratio between groups in this region, but there are no significant differences in CBF and ALFF between groups; the region where the matrix G value is 5 represents that there are significant differences in both CBF and the CBF / ALFF ratio between groups in this region, but there is no significant difference in ALFF between groups; the region where the matrix G value is 6 represents that there are significant differences in both ALFF and the CBF / ALFF ratio between groups in this region, but there is no significant difference in CBF between groups; the region where the matrix G value is 7 represents that there are significant differences in CBF, ALFF, and the CBF / ALFF ratio between groups in this region.

[0058] Obviously, the function output variable G reflects the consistency of multiple index features, that is, different values of G represent different situations of the consistency of multiple index features.

[0059] Step 3, visualization of consistency, specifically:

[0060] Perform color mapping on the result obtained in Step 2 and overlay it with the brain map background for display.

[0061] Specifically, use a color bar to map the non-zero values of the matrix G to different colors to distinguish different situations of consistency. This figure serves as an overlay layer; select a brain map in the same space (the Ch2 template is selected in this embodiment) as the background layer; finally, overlay the overlay layer on top of the background layer for display. The final effect diagram can fully display the consistency of different index features throughout the brain.

[0062] After the above processing in this embodiment, the obtained consistency visualization effect diagram is as Figure 5 shown.

[0063] Embodiment 2

[0064] The second embodiment of the present invention, which is different from the previous embodiment, is as follows:

[0065] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0067] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0068] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0069] Embodiment 3

[0070] The third embodiment of the present invention provides a brain image multi-index feature consistency analysis system, which is characterized in that it includes an organizing feature module, a constructing function and calculating result module, and a visualizing consistency module;

[0071] The organizing feature module converts the feature image of each brain image index to be analyzed into a mask;

[0072] The constructing function and calculating result module constructs a linear function and calculates the result according to the feature mask of each brain image index to be analyzed.

[0073] The visualizing consistency module performs color mapping on the result and superimposes it on the brain map background for display.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for analyzing the consistency of multiple index features of brain images, characterized by: include, Convert the characteristic image of each brain imaging index to be analyzed into a mask; According to the feature mask of each brain imaging index to be analyzed, a linear function is constructed and the result is calculated; The results were color mapped and displayed superimposed on the brain map background.

2. A method for analyzing consistency of multi-index features of brain images as claimed in claim 1, characterized in that: The characteristic image is an image used to determine whether an element is a characteristic, which is the indicator image itself at the individual level and the statistical parameter image at the group level.

3. A method for analyzing consistency of multi-index features of brain images as claimed in claim 2, characterized in that: The step of converting the characteristic image of each brain image index to be analyzed into a mask includes: assuming that the characteristic image of the nth brain image index is I n , I n is a three-dimensional matrix of a×b×c, where a, b, and c represent I n The number of elements in the X, Y, and Z directions; Assume that the nth brain image index feature is F n , F n is a three-dimensional matrix of a×b×c, and its values ​​consist of 0 and 1. Let the region that satisfies the characteristic condition be the set S n , then F n The calculation method is: Among them, I n (x, y, z) represents the matrix I n The value of the element at position X=x, Y=y, Z=z, F n (x, y, z) represents the matrix F n The value of the element at position X=x, Y=y, Z=z.

4. A method for analyzing consistency of multi-index features of brain images as claimed in claim 3, characterized in that: The constructing of the linear function and calculating the result includes: Assume that the output variable of the linear function is G, which is a three-dimensional matrix of a×b×c. When the number of brain imaging indicators to be analyzed is 2, the linear function is constructed as follows: G(x,y,z)=F1(x,y,z)+2×F2(x,y,z) Among them, F n (x, y, z) represents the matrix F n The value of the element at position X=x, Y=y, Z=z in the matrix G, n=1 or 2, G(x, y, z) represents the value of the element at position X=x, Y=y, Z=z in the matrix G, if and only if F1(x, y, z)=0 and F2(x, y, z)=0, G(x, y, z)=0; if and only if F1(x, y, z)=1 and F2(x, y, z)=0, G(x, y, z)=1; if and only if F1(x, y, z)=0 and F2(x, y, z)=0, G(x, y, z)=1; if and only if F1(x, y, z)=0 and F2(x , y, z)=1, G(x, y, z)=2; when and only when F1(x, y, z)=1 and F2(x, y, z)=1, G(x, y, z)=3; the area where the matrix G value is 0 represents the area that does not satisfy feature 1 or feature 2; the area where the matrix G value is 1 represents the area that satisfies feature 1 but not feature 2; the area where the matrix G value is 2 represents the area that satisfies feature 2 but not feature 1; the area where the matrix G value is 3 represents the area that satisfies both feature 1 and feature 2.

5. A method for analyzing consistency of multi-index features of brain images as claimed in claim 4, characterized in that: The constructing of a linear function and calculating the result also includes: When the number of brain imaging indicators to be analyzed is 3, the linear function is constructed as follows: G(x,y,z)=F1(x,y,z)+2×F2(x,y,z)+4×F3(x,y,z) Among them, F n (x, y, z) represents the matrix F n The value of the element at position X=x, Y=y, Z=z in the matrix G, n=1 or 2 or 3, G(x, y, z) represents the value of the element at position X=x, Y=y, Z=z in the matrix G, if and only if F1(x, y, z)=0 and F2(x, y, z)=0 and F3(x, y, z)=0, G(x, y, z)=0; if and only if F1(x, y, z)=1 and F2(x, y, z)=0 and F3(x, y, z)=0, G(x, y, z)=1; if and only if F1(x, y, z)=1 and F2(x, y, z)=0 and F3(x, y, z)=0, G(x, y, z)=1; if and only if F1(x, y, z) =0 and F2(x, y, z)=1 and F3(x, y, z)=0, G(x, y, z)=2; if and only if F1(x, y, z)=1 and F2(x, y, z)=1 and F3(x, y, z)=0, G(x, y, z)=3; if and only if F1(x, y, z)=0 and F2(x, y, z)=0 and F3(x, y, z)=1, G(x, y, z)=4; if and only if F1(x, y, z)=1 and F2(x, y, z)=0 and F3(x, y, z)= 1, G(x, y, z) = 5; if and only if F1(x, y, z) = 0 and F2(x, y, z) = 1 and F3(x, y, z) = 1, G(x, y, z) = 6; if and only if F1(x, y, z) = 1 and F2(x, y, z) = 1 and F3(x, y, z) = 1, G(x, y, z) = 7; the area with a matrix G value of 0 represents that the area does not meet feature 1, feature 2 or feature 3; the area with a matrix G value of 1 represents that the area meets feature 1 but does not meet feature 2 or feature 3; the matrix G value The area with a matrix G value of 2 represents an area that satisfies feature 2 but not feature 1 and feature 3; the area with a matrix G value of 3 represents an area that satisfies both feature 1 and feature 2 but not feature 3; the area with a matrix G value of 4 represents an area that satisfies feature 3 but not feature 1 and feature 2; the area with a matrix G value of 5 represents an area that satisfies both feature 1 and feature 3 but not feature 2; the area with a matrix G value of 6 represents an area that satisfies both feature 2 and feature 3 but not feature 1; the area with a matrix G value of 7 represents an area that satisfies both feature 1, feature 2, and feature 3.

6. A method for analyzing consistency of multi-index features of brain images as claimed in claim 5, characterized in that: The color mapping of the results and displaying them overlaid with the brain map background includes using a color bar to map non-zero values ​​of the matrix G into different colors to distinguish different consistency situations as an overlay layer; Select the mind map of the same space as the background layer; overlay the overlay layer on the background layer for display.

7. A system based on the method for analyzing consistency of multi-index features of brain images according to any one of claims 1 to 6, characterized in that: Including, feature arrangement module, function construction and result calculation module, and consistency visualization module; The feature arrangement module converts the feature image of each brain image index to be analyzed into a mask; The module for constructing functions and calculating results constructs a linear function and calculates the results according to the feature mask of each brain image index to be analyzed. The visualization consistency module performs color mapping on the results and displays them superimposed on the brain map background.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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