Data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a data processing method and device, equipment and a storage medium. According to the scheme, the original brain image obtained based on the DTI technology is subjected to preprocessing such as noise reduction processing, artifact removal processing and / or brain tissue extraction processing, and the generation accuracy of subsequent anisotropic fractional images and color diffusion tensor diagrams can be improved. According to the affine transformation matrix required for transforming the anisotropic fraction image into the standard anisotropic fraction image, the color diffusion tensor image is transformed, the direction correction of the brain image in the color diffusion tensor image can be completed, and the influence of the inclination of the brain image in the color diffusion tensor image on the subsequent analysis process is avoided. In addition, by adopting the automatic searching scheme for the region of interest of the fiber bundle provided by the embodiment of the invention, the region of interest of the combined fiber bundle and the region of interest of the projected fiber bundle can be quickly, accurately and automatically determined, so that the reliability of the finally obtained diffusion tensor imaging analysis index along the peripheral gap of the blood vessel can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of computer analysis of medical images, and in particular to a data processing method, device, equipment and storage medium. Background Art

[0002] Diffusion tensor imaging (DTI) is a method for describing brain structure and a special form of magnetic resonance imaging. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) is a method for inferring the diffusion properties of fluid in the PVS (perivascular space) by using the relative geometric organization of white matter fiber bundles and perivascular spaces. DTI-ALPS evaluates the function of the cerebral glymphatic system by comparing the diffusion properties of the associated fibers and projection fibers along the deep medullary vein and perpendicular to the deep medullary vein.

[0003] In the existing DTI-ALPS analysis method, doctors are required to manually outline the association fiber bundle (Association) region of interest (ROI) and the projection fiber bundle (Projection) region of interest from the diffusion tensor map. Usually, the size of the association fiber bundle region of interest and the projection fiber bundle region of interest are very small, at the millimeter level. Manually outlining the region of interest not only requires the operator's experience and professionalism, but also requires a lot of the operator's time. In practical applications, the association fiber bundle region of interest and the projection fiber bundle region of interest drawn by different operators have differences in size, shape, position, etc., and the association fiber bundle region of interest and the projection fiber bundle region of interest drawn by the same operator at different times will also have differences in size, shape, position, etc. It can be seen that manually outlining the region of interest is not only inefficient, but also has the problem of poor consistency, which leads to poor reliability and repeatability of the DTI-ALPS indicators obtained in the final analysis. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a data processing method, device, equipment and storage medium that solve the above problems or at least partially solve the above problems.

[0005] Therefore, in one embodiment of the present application, a data processing method is provided, comprising:

[0006] Obtain original brain images based on diffusion tensor imaging (DTI) technology;

[0007] Preprocessing the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing;

[0008] generating a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image;

[0009] Get the preset standard anisotropy fractional image;

[0010] Determining an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image;

[0011] transforming the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map;

[0012] Determining a plurality of target layers that meet preset conditions in the target color diffusion tensor map; the Z-axis coordinate values ​​of a plurality of voxels included in any target layer are the same;

[0013] According to the same traversal method, a plurality of voxel pairs related to the first hemisphere in each target layer are traversed; the plurality of target layers include the first target layer; the traversal method includes: determining the Y-axis coordinate value range corresponding to the first target layer and the X-axis coordinate value range of the first target layer related to the first hemisphere; traversing a plurality of target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing a plurality of voxel pairs corresponding to each target Y-axis coordinate value in the plurality of target Y-axis coordinate values ​​within the area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the plurality of voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first hemisphere is the left hemisphere or the right hemisphere;

[0014] For each voxel pair obtained through traversal, a first candidate interest group and a second candidate interest group corresponding to the voxel pair are determined in the target layer where the voxel pair is located; the first candidate interest group includes: a joint fiber bundle candidate interest group centered on the first voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the second voxel in the voxel pair; the second candidate interest group includes: a joint fiber bundle candidate interest group centered on the second voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest group and the projected fiber bundle candidate interest group are preset shape areas with preset sizes;

[0015] Determine, according to the green value and the blue value of the voxel in the target color diffusion tensor image, the average green value in the joint fiber bundle candidate region of interest and the average blue value in the projection fiber bundle candidate region of interest in each candidate region of interest combination;

[0016] According to the candidate region of interest combination with the largest sum of the average green value in the candidate region of interest of the joint fiber bundle and the average blue value in the candidate region of interest of the projection fiber bundle, the joint fiber bundle region of interest and the projection fiber bundle region of interest are determined;

[0017] According to the tensors of the voxels in the combined fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image, the diffusion tensor imaging analysis index along the perivascular space is determined.

[0018] Optionally, the combined fiber bundle candidate region of interest and the projected fiber bundle candidate region of interest are circular areas with preset diameters.

[0019] Optionally, the preset conditions include: a preset Z-axis coordinate value range.

[0020] Optionally, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0021] Optionally, the resolution of the standard anisotropy fractional image is greater than the resolution of the anisotropy fractional image. In this way, the affine transformation can not only achieve direction correction but also improve resolution.

[0022] In one embodiment of the present application, a data processing device is provided, including:

[0023] The first acquisition module is used to acquire the original brain image based on the diffusion tensor imaging DTI technology;

[0024] A preprocessing module, used to preprocess the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing;

[0025] A generating module, used for generating an anisotropy fractional image and a color diffusion tensor image according to the preprocessed brain image;

[0026] A second acquisition module is used to acquire a preset standard anisotropy fractional image;

[0027] A first determining module, configured to determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image;

[0028] A transformation module, used for transforming the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map;

[0029] A second determination module is used to determine a plurality of target layers that meet a preset condition in the target color diffusion tensor map; the Z-axis coordinate values ​​of a plurality of voxels included in any target layer are the same;

[0030] A traversal module, used for traversing multiple voxel pairs related to the first half of the brain in each target layer in the same traversal mode; the multiple target layers include the first target layer; the traversal mode includes: determining the Y-axis coordinate value range corresponding to the first target layer and the X-axis coordinate value range of the first target layer related to the first half of the brain; traversing multiple target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing multiple voxel pairs corresponding to each target Y-axis coordinate value in the multiple target Y-axis coordinate values ​​within the area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the multiple voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first half of the brain is the left half of the brain or the right half of the brain;

[0031] The third determination module is used to determine, for each voxel pair obtained through traversal, a first candidate interest group and a second candidate interest group corresponding to the voxel pair in the target layer where the voxel pair is located; the first candidate interest group includes: a joint fiber bundle candidate interest group centered on the first voxel in the voxel pair and a projection fiber bundle candidate interest group centered on the second voxel in the voxel pair; the second candidate interest group includes: a joint fiber bundle candidate interest group centered on the second voxel in the voxel pair and a projection fiber bundle candidate interest group centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest group and the projection fiber bundle candidate interest group are preset shape areas with preset sizes;

[0032] A fourth determination module is used to determine the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI in each candidate ROI combination according to the green value and the blue value of the voxel in the target color diffusion tensor map;

[0033] A fifth determination module is used to determine the combined fiber bundle region of interest and the projected fiber bundle region of interest according to the candidate region of interest combination with the largest sum of the average green value in the combined fiber bundle candidate region of interest and the average blue value in the projected fiber bundle candidate region of interest;

[0034] The sixth determination module is used to determine the diffusion tensor imaging analysis index along the perivascular space according to the tensors of the voxels in the joint fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image.

[0035] Optionally, the combined fiber bundle candidate region of interest and the projected fiber bundle candidate region of interest are circular areas with preset diameters.

[0036] Optionally, the preset conditions include: a preset Z-axis coordinate value range.

[0037] Optionally, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0038] Optionally, a resolution of the standard fractional anisotropy image is greater than a resolution of the fractional anisotropy image.

[0039] In this way, affine transformation can not only achieve direction correction but also improve resolution.

[0040] In one embodiment of the present application, an electronic device is provided, including: a memory and a processor, wherein:

[0041] The memory is used to store programs;

[0042] The processor is coupled to the memory, and is used to execute the program stored in the memory to implement any of the methods described above.

[0043] In one embodiment of the present application, a computer-readable storage medium storing a computer program is provided, and when the computer program is executed by a computer, any of the above-mentioned methods can be implemented.

[0044] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor images can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor image is transformed to complete the direction correction of the brain image in the color diffusion tensor image, so as to avoid the tilt of the brain image in the color diffusion tensor image from affecting the subsequent analysis process.

[0045] In addition, in this scheme, multiple voxel pairs that meet the requirements are first located by traversal, and the requirements are: the Z-axis coordinate values ​​of the two voxels in the same voxel pair are equal, and the Y-axis coordinate values ​​of the two voxels are equal. Then, combined with the preset shape under the preset size, the first candidate interest combination and the second candidate interest combination corresponding to each voxel pair are automatically determined; the first candidate interest combination includes: the joint fiber bundle candidate interest area centered on the first voxel in the voxel pair and the projected fiber bundle candidate interest area centered on the second voxel in the voxel pair; the second candidate interest combination includes: the joint fiber bundle candidate interest area centered on the second voxel in the voxel pair and the projected fiber bundle candidate interest area centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest area and the projected fiber bundle candidate interest area are preset shape areas under the preset size. Finally, because each voxel in the color diffusion tensor map corresponds to three color values: red value, green value and blue value, where red represents the diffusion corresponding to the voxel along the X-axis direction; green represents the diffusion corresponding to the voxel along the Y-axis direction; blue represents the diffusion corresponding to the voxel along the Z-axis direction. Among them, the joint cellulose is along the Y-axis direction, and the projected cellulose is along the Z-axis direction. Therefore, according to the green value and blue value of the voxel in the target color diffusion tensor map, the average green value in the joint fiber bundle candidate region of interest and the average blue value in the projected fiber bundle candidate region of interest in each candidate region of interest combination are determined; according to the candidate region of interest combination with the largest sum of the average green value in the joint fiber bundle candidate region of interest and the average blue value in the projected fiber bundle candidate region of interest, the joint fiber bundle region of interest and the projected fiber bundle region of interest are automatically determined. The search scheme provided by the embodiment of the present application can quickly and accurately automatically determine the joint fiber bundle region of interest and the projected fiber bundle region of interest, thereby improving the reliability and repeatability of the ALPS index obtained by the final analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1a A flowchart of a search method provided in an embodiment of the present application;

[0048] Figure 1b An example diagram of ROI marking provided in an embodiment of the present application;

[0049] Figure 1cAn experimental comparison diagram provided for an embodiment of the present application;

[0050] Figure 2 A flowchart of a data processing method provided in one embodiment of the present application;

[0051] Figure 3 A flowchart of a data processing method provided in yet another embodiment of the present application;

[0052] Figure 4 A structural block diagram of a search device provided in an embodiment of the present application;

[0053] Figure 5 A structural block diagram of a data processing device provided in one embodiment of the present application;

[0054] Figure 6 A structural block diagram of a data processing device provided in yet another embodiment of the present application;

[0055] Figure 7 A structural block diagram of an electronic device provided in yet another embodiment of the present application;

[0056] Figure 8 A color diffusion tensor image before affine transformation is provided for an embodiment of the present application;

[0057] Fig. 9 A color diffusion tensor image after affine transformation is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below according to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0059] In addition, some of the processes described in the specification, claims and the above-mentioned figures of the present application include multiple operations that appear in a specific order, and these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to different types.

[0060] Figure 1a A flow chart of a method for searching for fiber bundle regions of interest provided in one embodiment of the present application is shown. The execution subject of the method may be a client or a server. The client may be a hardware with an embedded program integrated in a terminal, or an application software installed in the terminal, or a tool software embedded in the terminal operating system, etc., which is not limited in the present embodiment of the application. The terminal may be any terminal device including a mobile phone, a tablet computer, a vehicle-mounted terminal device, etc. The server may be a common server, a cloud or a virtual server, etc., which is not specifically limited in the present embodiment of the application. Figure 1a As shown, the method comprises the following steps:

[0061] 101. Obtain the original brain image based on diffusion tensor imaging DTI technology.

[0062] The human brain is imaged by DTI technology to obtain original brain images. There are multiple original brain images, and multiple original brain images can be regarded as an image sequence.

[0063] 102. Preprocess the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing.

[0064] Specifically, preprocessing the original brain image may include the following sub-steps:

[0065] (a) Image denoising.

[0066] (b) Removal of Gibbs artifacts.

[0067] (c) Motion and eddy current distortion correction.

[0068] (d) Bias field correction.

[0069] (e) Extraction of brain tissue.

[0070] Among them, steps b, c and d are all artifact removal processes.

[0071] The generation accuracy of color anisotropy fractional images and color diffusion tensor maps can be improved through preprocessing.

[0072] 103. Generate a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image.

[0073] A fractional anisotropy (FA) image is generated based on the original brain image. A color diffusion tensor image is generated based on the original brain image.

[0074] Each voxel in the color diffusion tensor map corresponds to a tensor (ie, diffusion tensor) and three color values: red value, green value, and blue value.

[0075] The tensor matrix D is:

[0076]

[0077] Among them, D includes 9 non-zero factors, among which, due to symmetry, D xy =D yx , D xz =D zx , D yz =D zy . 3 non-zero factors (D xx , D yy and D zz ) along the main diagonal of the tensor, called the intrinsic value. xx is the diffusion coefficient of water molecules in the X direction; D xx is the diffusion coefficient of water molecules in the X direction; D xx is the diffusion coefficient of water molecules in the X direction; D xx is the diffusion coefficient of water molecules in the X direction; D xy and D yx is the diffusion correlation between water molecules in the X direction and the Y direction; D xz and D zx is the diffusion correlation between water molecules in the X direction and the Z direction; D yz and D zy is the diffusion correlation between water molecules in the Y direction and the Z direction.

[0078] The color diffusion tensor image determines the direction of white matter fiber according to the principle that the direction of the maximum eigenvector of voxel diffusion determines the course of white matter fibers. The main eigenvectors in the X, Y, and Z axis directions of the color diffusion tensor image are matched with red, green, and blue colors respectively. The X axis direction corresponds to the left and right direction of the human brain; the Y axis direction corresponds to the front and back direction of the human brain; and the Z axis direction corresponds to the up and down direction of the human brain. Among them, red represents the diffusion corresponding to the voxel along the X axis direction; green represents the diffusion corresponding to the voxel along the Y axis direction; and blue represents the diffusion corresponding to the voxel along the Z axis direction. The larger the red value, the stronger the diffusion corresponding to the voxel along the X axis direction; the larger the green value, the stronger the diffusion corresponding to the voxel along the Y axis direction; and the larger the blue value, the stronger the diffusion corresponding to the voxel along the Z axis direction. Among them, the joint cellulose is along the Y axis direction, and the projected cellulose is along the Z axis direction. It can be seen that the green area in the color diffusion tensor image is the area where the joint fiber bundle is located; the blue area in the color diffusion tensor image is the area where the projected fiber bundle is located.

[0079] 104. Obtain a preset standard anisotropy fractional image.

[0080] In actual applications, when using DTI equipment to image the user's brain, it is difficult to require the user to position his or her brain in the same standard posture as required by the imaging. In other words, if the brain image obtained by imaging is more or less tilted, then the brain image in the color diffusion tensor map generated based on the brain image will also be tilted.

[0081] In some analysis scenarios, such as the ALPS indicator analysis scenario, the tilt of the brain image in the color diffusion tensor map will lead to poor accuracy of the final analysis results. This is because: in the color diffusion tensor map, the direction of each fiber bundle is directly determined by the X-axis direction, Y-axis direction, and Z-axis direction of the map. Once the brain image in the color diffusion tensor map is tilted, the X-axis direction, Y-axis direction, and Z-axis direction of the color diffusion tensor map cannot accurately describe the direction of each fiber bundle.

[0082] In order to solve the above problems, a standard anisotropy fractional image can be prepared in advance. The posture of the brain image in the standard anisotropy fractional image does not have any tilt or the like. Taking the ALPS index analysis scenario as an example, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0083] 105. Determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image.

[0084] The calculation principle of the affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image can refer to the existing affine transformation principle, which is not described in detail in the embodiment of the present application.

[0085] The standard anisotropy fractional image can be stored in the MNI (Montreal Neurological Institute) standard space. The affine transformation matrix required to register the anisotropy fractional image to the MNI standard space can be determined based on the anisotropy fractional image and the standard color anisotropy fractional image in the MNI standard space. The affine transformation matrix is ​​used to register the anisotropy fractional image with the standard anisotropy fractional image.

[0086] 106. Transform the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map.

[0087] According to the affine transformation matrix, the color diffusion tensor image is transformed to achieve the direction correction of the color diffusion tensor image. The left-right direction of the brain image in the target color diffusion tensor image is parallel to the X-axis of the target color diffusion tensor image, the front-back direction is parallel to the Y-axis of the target color diffusion tensor image, and the up-down direction is parallel to the Z-axis of the target color diffusion tensor image.

[0088] In this embodiment, since both the anisotropy fractional image and the color diffusion tensor image are generated based on the same brain image, the affine transformation matrix required for transforming the color diffusion tensor image into the standard color diffusion tensor image should be consistent with the affine transformation matrix required for transforming the anisotropy fractional image into the standard anisotropy fractional image. However, the calculation process of the affine transformation matrix required for transforming the color diffusion tensor image into the standard color diffusion tensor image is relatively complicated, and the calculation process of the affine transformation matrix required for transforming the anisotropy fractional image into the standard anisotropy fractional image is relatively simple. Therefore, the embodiment of the present application chooses to calculate the affine transformation matrix required for transforming the anisotropy fractional image into the standard anisotropy fractional image, which can reduce the difficulty of implementing the solution.

[0089] In addition, in practical applications, due to the accuracy of the image acquisition equipment, the resolution of the original brain image may be low. Then, the resolution of the anisotropy fractional image and the color diffusion tensor map generated based on the original brain image with low resolution is also low. In order to improve the resolution of the color diffusion tensor map, the resolution of the above-mentioned standard anisotropy fractional image can be set higher. In this way, through affine transformation, not only the direction of the color diffusion tensor map can be corrected, but also the image resolution can be improved. Specifically, the resolution of the standard anisotropy fractional image is higher than the resolution of the above-mentioned anisotropy fractional image.

[0090] Figure 8 Shown is the color diffusion tensor map before the affine transformation; Fig. 9 The color diffusion tensor image after affine transformation is shown. It can be clearly seen that after affine transformation, the direction of the brain image in the color diffusion tensor image is corrected and the resolution is improved.

[0091] 107. Determine a plurality of target layers satisfying preset conditions in the target color diffusion tensor map.

[0092] The Z-axis coordinate values ​​of the multiple voxels contained in any target layer are the same.

[0093] Since the combined fiber bundle and the projected fiber bundle are distributed around the uppermost layer of the lateral ventricle body in the human brain, the traversal range can be screened out by limiting the Z-axis coordinate value of the image to reduce the amount of calculation and traversal time. The preset conditions include: a preset Z-axis coordinate value range. The preset Z-axis coordinate value range can be determined based on actual experience.

[0094] The Z-axis coordinate values ​​of the multiple voxels contained in any one of the multiple target layers are all within a preset Z-axis coordinate value range.

[0095] 108. Using the same traversal method, traverse multiple voxel pairs related to the first hemisphere in each target layer.

[0096] Among them, the multiple target layers include a first target layer; the first target layer is any one of them.

[0097] Generally speaking, the center point of the combined fiber bundle area of ​​interest in the first hemisphere has the same Z-axis coordinate value as the center point of the projection fiber bundle area of ​​interest in the first hemisphere; the center point of the combined fiber bundle area of ​​interest in the first hemisphere has the same Y-axis coordinate value as the center point of the projection fiber bundle area of ​​interest in the first hemisphere, that is, the direction of the line connecting the two center points is consistent with the direction of the deep veins.

[0098] Therefore, the traversal is performed as follows:

[0099] Determine the Y-axis coordinate value range corresponding to the first target layer and the X-axis coordinate value range of the first target layer related to the first hemisphere; within the Y-axis coordinate value range, traverse a plurality of target Y-axis coordinate values; traverse a plurality of voxel pairs corresponding to each target Y-axis coordinate value in the plurality of target Y-axis coordinate values ​​within the area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the plurality of voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first hemisphere is the left hemisphere or the right hemisphere.

[0100] The first target layer refers to any one of the plurality of target layers. The X-axis coordinate value range of the first target layer for the left hemisphere is different from the X-axis coordinate value range of the first target layer for the right hemisphere, and the union of the two is the X-axis coordinate value range of the first target layer.

[0101] 109. For each voxel pair obtained by the traversal, determine a first candidate combination of interest and a second candidate combination of interest corresponding to the voxel pair in the target layer where the voxel pair is located.

[0102] Among them, the first candidate interest combination includes: a joint fiber bundle candidate interest area centered on the first voxel in the voxel pair and a projected fiber bundle candidate interest area centered on the second voxel in the voxel pair; the second candidate interest combination includes: a joint fiber bundle candidate interest area centered on the second voxel in the voxel pair and a projected fiber bundle candidate interest area centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest area and the projected fiber bundle candidate interest area are preset shape areas with preset sizes.

[0103] For example: the candidate region of interest of the combined fiber bundle and the candidate region of interest of the projected fiber bundle are rectangular regions with preset length and width; another example: the candidate region of interest of the combined fiber bundle and the candidate region of interest of the projected fiber bundle are circular regions with preset diameters. The numerical values ​​of the preset length, width and preset diameter can be set according to actual experience, and the embodiments of the present application do not specifically limit this. For example, the preset diameter can be 5 mm.

[0104] 110. Determine, according to the green value and the blue value of the voxel in the target color diffusion tensor image, the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI in each candidate ROI combination.

[0105] The multiple candidate ROI combinations determined in the above step 109 include a first candidate ROI combination. The first candidate ROI combination is any one of the above multiple candidate ROI combinations; the first candidate ROI combination includes: a first joint fiber bundle candidate ROI and a first projected fiber bundle candidate ROI. The green values ​​of multiple voxels in the first joint fiber bundle candidate ROI in the target color diffusion tensor map are averaged to obtain an average green value in the first joint fiber bundle candidate ROI; the blue values ​​of multiple voxels in the first projected fiber bundle candidate ROI in the target color diffusion tensor map are averaged to obtain an average blue value in the first projected fiber bundle candidate ROI.

[0106] 111. Determine the combined fiber bundle region of interest and the projection fiber bundle region of interest based on the candidate region of interest combination with the largest sum of the average green value in the combined fiber bundle candidate region of interest and the average blue value in the projection fiber bundle candidate region of interest.

[0107] For each candidate ROI combination, the sum of the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI can be calculated; the candidate joint fiber bundle ROI in the candidate ROI combination with the largest sum value is taken as the joint fiber bundle ROI; the candidate projection fiber bundle ROI in the candidate ROI combination with the largest sum value is taken as the projection fiber bundle ROI.

[0108] In this embodiment, by maximizing the green value of the combined fiber bundle region of interest and the blue value of the projected fiber bundle, the influence of other fiber bundle directions can be excluded as much as possible.

[0109] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor images can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor image is transformed to complete the direction correction of the brain image in the color diffusion tensor image, so as to avoid the tilt of the brain image in the color diffusion tensor image from affecting the subsequent analysis process.

[0110] In addition, in this scheme, multiple voxel pairs that meet the requirements are first located by traversal. Then, combined with the preset shape under the preset size, the first candidate interest combination and the second candidate interest combination for each voxel pair are automatically determined; the first candidate interest combination includes: the joint fiber bundle candidate interest area centered on the first voxel in the voxel pair and the projection fiber bundle candidate interest area centered on the second voxel in the voxel pair; the second candidate interest combination includes: the joint fiber bundle candidate interest area centered on the second voxel in the voxel pair and the projection fiber bundle candidate interest area centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest area and the projection fiber bundle candidate interest area are preset shape areas under the preset size. Finally, because in the color diffusion tensor map, each voxel corresponds to three color values: red value, green value and blue value, where red represents the diffusion corresponding to the voxel along the X-axis direction; green represents the diffusion corresponding to the voxel along the Y-axis direction; blue represents the diffusion corresponding to the voxel along the Z-axis direction. Among them, the joint fiber is along the Y-axis direction, and the projected fiber is along the Z-axis direction, so according to the green value and blue value of the voxel in the target color diffusion tensor map, the average green value in the joint fiber bundle candidate area of ​​interest and the average blue value in the projected fiber bundle candidate area of ​​interest in each candidate area of ​​interest combination are determined; according to the candidate area of ​​interest combination with the largest sum of the average green value in the joint fiber bundle candidate area of ​​interest and the average blue value in the projected fiber bundle candidate area of ​​interest, the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest are automatically determined. The search scheme provided by the embodiment of the present application can quickly and accurately automatically determine the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest, which provides an accurate data basis for subsequent analysis.

[0111] In practical applications, after the combined fiber bundle region of interest and the projected fiber bundle region of interest are determined in the target color diffusion tensor image, the diffusion tensor imaging analysis index along the perivascular space can be determined based on the tensors of the voxels in the combined fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image.

[0112] Specifically, the diffusion tensor imaging analysis index along the perivascular space can be calculated according to the following formula:

[0113]

[0114] Among them, D xxproj Refers to the projection fiber bundle within the area of ​​interest D xx Average value; D yyproj Refers to the projection fiber bundle within the area of ​​interest D yy Average value. xxassoci Refers to the region of interest of the combined fiber bundle D xx Average value; D zzassoci Refers to the region of interest of the combined fiber bundle D zz The mean() function is used to calculate the mean.

[0115] The technical solution provided in the embodiment of the present application can quickly and accurately automatically determine the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest from the target color tensor map, that is, the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest obtained by the technical solution provided in the embodiment of the present application have good accuracy and consistency, which can improve the reliability of the diffusion tensor imaging analysis index finally obtained along the perivascular space. It should be supplemented that the ALPS index is calculated separately for the left and right hemispheres in the above steps, that is, after obtaining the ALPS index of the left and right hemispheres, the mean of the ALPS index of the left and right hemispheres is obtained for statistical analysis. Specifically, for the right hemisphere, according to the green value and blue value of the voxel in the target color diffusion tensor map, the joint fiber bundle area of ​​interest of the right hemisphere and the projected fiber bundle area of ​​interest of the right hemisphere are determined from the target color tensor map by traversal; according to the tensor of the voxels in the joint fiber bundle area of ​​interest of the right hemisphere and the projected fiber bundle area of ​​interest of the right hemisphere in the target color diffusion tensor map, the diffusion tensor imaging analysis index of the right hemisphere along the perivascular space is determined.

[0116] In the embodiment of the present application, the preprocessing of the original DTI to the final generation of the ALPS value is all completed automatically, without the need for manual correction during the process. The generated ROI can also be superimposed on the color FA image for rapid quality control. Figure 1b The left side of the middle arrow shows the target colored diffusion tensor image with the joint fiber bundle area of ​​interest and the projection fiber bundle area of ​​interest marked. Figure 1b The right side of the middle arrow shows the D corresponding to the target color diffusion tensor image. xx Component diagram, D yy Component diagram and D zz Component maps, each component map is marked with the combined fiber bundle area of ​​interest and the projection fiber bundle area of ​​interest.

[0117] To verify the feasibility of this method, we compared the automatic method with two experts who had received extraction training (or operators A and B) on 100 random subjects, and compared three methods: qualitative visual interpretation, calculation of Pearson correlation based on raw data, and calculation of kappa coefficient after conversion to quartiles for consistency analysis. We also counted the time for manual annotation. Figure 1c The mean values ​​of left and right brain ALPS of 100 patients determined by this protocol (auto in the figure), observer A and observer B (Mean ALPS in the figure) are shown. The results of the qualitative method of visual interpretation of the image showed that it was impossible to distinguish whether it was automatic ALPS or manual ALPS. The Pearson correlation analysis results shown in Table 1 showed that the correlation coefficients of auto with A and B were 0.794 and 0.827, which were higher than the correlation between A and B (R = 0.790); the quartile-based consistency analysis results shown in Table 2 showed that the kappa coefficients of auto with A and B were 0.581 and 0.608, which were higher than the consistency between A and B (kappa = 0.579). The above results show that the correlation and consistency of the automated analysis method with the observer are greater than the differences between the observers, indicating the high reliability of the automated analysis method. And the calculation process takes less than 1 second, which is much better than the average time efficiency of the observer (A takes about 130 seconds / subject, B takes about 140 seconds / subject).

[0118] In Table 1, the results of Pearson correlation analysis showed that the correlation coefficients between auto and A and B were 0.794 and 0.827, which were higher than the correlation between A and B. In Table 2, the kappa coefficients between auto and A and B were 0.581 and 0.608, which were higher than the consistency between A and B. The above results showed that the correlation and consistency between the automated analysis method and the observer were greater than the differences between the observers, indicating the high reliability of the automated analysis method.

[0119] Table 1:

[0120]

[0121] Table 2:

[0122]

[0123] Figure 2A flow chart of a data processing method provided by another embodiment of the present application is shown. The executor of the method may be a client or a server. The client may be a hardware with an embedded program integrated on a terminal, or an application software installed in the terminal, or a tool software embedded in the terminal operating system, etc., which is not limited in the embodiments of the present application. The terminal may be any terminal device including a mobile phone, a tablet computer, a vehicle-mounted terminal device, etc. The server may be a common server, a cloud or a virtual server, etc., which is not specifically limited in the embodiments of the present application. Figure 2 As shown, the method includes:

[0124] 201. Obtain the original brain image based on diffusion tensor imaging DTI technology.

[0125] 202. Preprocess the original brain image to obtain a preprocessed brain image.

[0126] The preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing.

[0127] 203. Generate a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image.

[0128] 204. Obtain a preset standard anisotropy fractional image.

[0129] 205. Determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image.

[0130] 206. Transform the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map.

[0131] 207. Determine a plurality of target layers that meet preset conditions in the target color diffusion tensor map.

[0132] The Z-axis coordinate values ​​of multiple voxels contained in any target layer are the same.

[0133] 208. Using the same traversal method, traverse multiple voxel pairs related to the first hemisphere in each target layer.

[0134] The multiple target layers include a first target layer; the traversal method includes: determining a Y-axis coordinate value range corresponding to the first target layer and an X-axis coordinate value range of the first target layer related to the first half of the brain; traversing multiple target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing multiple voxel pairs corresponding to each target Y-axis coordinate value in the multiple target Y-axis coordinate values ​​within an area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the multiple voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first half of the brain is the left hemisphere or the right hemisphere.

[0135] 209. For each voxel pair obtained through the traversal, determine a first candidate combination of interest and a second candidate combination of interest corresponding to the voxel pair in the target layer where the voxel pair is located.

[0136] The first candidate interest combination includes: a joint fiber bundle candidate interest region centered on the first voxel in the voxel pair and a projected fiber bundle candidate interest region centered on the second voxel in the voxel pair; the second candidate interest combination includes: a joint fiber bundle candidate interest region centered on the second voxel in the voxel pair and a projected fiber bundle candidate interest region centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest region and the projected fiber bundle candidate interest region are preset shape areas with preset sizes.

[0137] 210. Determine, according to the green value and the blue value of the voxel in the target color diffusion tensor image, the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI in each candidate ROI combination.

[0138] 211. Determine the combined fiber bundle region of interest and the projection fiber bundle region of interest based on the candidate region of interest combination with the largest sum of the average green value in the combined fiber bundle candidate region of interest and the average blue value in the projection fiber bundle candidate region of interest.

[0139] 212. Determine diffusion tensor imaging analysis indicators along the perivascular space according to the tensors of the voxels in the combined fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image.

[0140] The specific implementation process of the above steps 201 to 212 can refer to the corresponding content in the above embodiments, which will not be repeated here.

[0141] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor images can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor image is transformed to complete the direction correction of the brain image in the color diffusion tensor image, so as to avoid the tilt of the brain image in the color diffusion tensor image from affecting the subsequent analysis process.

[0142] In addition, in this scheme, multiple voxel pairs that meet the requirements are first located by traversal. Then, combined with the preset shape under the preset size, the first candidate interest combination and the second candidate interest combination for each voxel pair are automatically determined; the first candidate interest combination includes: the joint fiber bundle candidate interest area centered on the first voxel in the voxel pair and the projection fiber bundle candidate interest area centered on the second voxel in the voxel pair; the second candidate interest combination includes: the joint fiber bundle candidate interest area centered on the second voxel in the voxel pair and the projection fiber bundle candidate interest area centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest area and the projection fiber bundle candidate interest area are preset shape areas under the preset size. Finally, because in the color diffusion tensor map, each voxel corresponds to three color values: red value, green value and blue value, where red represents the diffusion corresponding to the voxel along the X-axis direction; green represents the diffusion corresponding to the voxel along the Y-axis direction; blue represents the diffusion corresponding to the voxel along the Z-axis direction. Among them, the joint cellulose is along the Y-axis direction, and the projected cellulose is along the Z-axis direction, so according to the green value and blue value of the voxel in the target color diffusion tensor map, the average green value in the joint fiber bundle candidate area of ​​interest and the average blue value in the projected fiber bundle candidate area of ​​interest in each candidate area of ​​interest combination are determined; according to the candidate area of ​​interest combination with the largest sum of the average green value in the joint fiber bundle candidate area of ​​interest and the average blue value in the projected fiber bundle candidate area of ​​interest, the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest are automatically determined. The search scheme provided by the embodiment of the present application can automatically and quickly determine the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest, which provides an accurate data basis for the subsequent diffusion tensor imaging analysis along the perivascular space, so that the diffusion tensor imaging analysis index finally determined along the perivascular space has a higher reliability.

[0143] It should be noted here that: for the contents of each step not fully described in detail in the method provided in the embodiment of the present application, please refer to the corresponding contents in the above embodiment, which will not be repeated here. In addition, in addition to the above steps, the method provided in the embodiment of the present application may also include other parts or all of the steps in the above embodiments, which can be specifically referred to the corresponding contents in the above embodiments, which will not be repeated here.

[0144] Figure 3 A flow chart of a data processing method provided by another embodiment of the present application is shown. The executor of the method may be a client or a server. The client may be a hardware with an embedded program integrated on a terminal, or an application software installed in the terminal, or a tool software embedded in the terminal operating system, etc., which is not limited in the embodiments of the present application. The terminal may be any terminal device including a mobile phone, a tablet computer, a vehicle-mounted terminal device, etc. The server may be a common server, a cloud or a virtual server, etc., which is not specifically limited in the embodiments of the present application. Figure 3 As shown, the method includes:

[0145] 301. Obtaining original brain images based on diffusion tensor imaging (DTI) technology;

[0146] 302. Preprocess the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing;

[0147] 303. Generate a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image;

[0148] 304. Obtain a preset standard anisotropy fractional image;

[0149] 305. Determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image;

[0150] 306. Transform the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map;

[0151] 307. Determine a joint fiber bundle region of interest and a projection fiber bundle region of interest in the target color diffusion tensor image;

[0152] 308. Determine diffusion tensor imaging analysis indicators along the perivascular space according to the tensors of the voxels in the joint fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image.

[0153] The specific implementation of the above 301 to 306 and 308 can refer to the corresponding content in the above embodiments, which will not be repeated here.

[0154] In the above 307 , in one example, in response to a delineation operation triggered by the user's target color diffusion tensor image, a joint fiber bundle region of interest and a projection fiber bundle region of interest may be determined in the target color diffusion tensor image.

[0155] In another example, the automatic search may be performed according to the search method of the fiber bundle region of interest in the above embodiments. For the specific search process, reference may be made to the corresponding contents in the above embodiments.

[0156] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor maps can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor map is transformed to complete the direction correction of the brain image in the color diffusion tensor map, providing an accurate data basis for the subsequent diffusion tensor imaging analysis of the perivascular space, so that the final determined diffusion tensor imaging analysis index of the perivascular space has a higher accuracy.

[0157] Optionally, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0158] Optionally, a resolution of the standard fractional anisotropy image is greater than a resolution of the fractional anisotropy image.

[0159] It should be noted here that: for the contents of each step not fully described in detail in the method provided in the embodiment of the present application, please refer to the corresponding contents in the above embodiment, which will not be repeated here. In addition, in addition to the above steps, the method provided in the embodiment of the present application may also include other parts or all of the steps in the above embodiments, which can be specifically referred to the corresponding contents in the above embodiments, which will not be repeated here.

[0160] Figure 4 FIG. 2 shows a structural block diagram of a device for searching a fiber bundle region of interest provided by an embodiment of the present application. Figure 4 As shown, the device comprises:

[0161] A first acquisition module 401 is used to acquire an original brain image based on diffusion tensor imaging DTI technology;

[0162] A preprocessing module 402 is used to preprocess the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing;

[0163] A generating module 403, for generating a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image;

[0164] The second acquisition module 404 is used to acquire a preset standard anisotropy fractional image;

[0165] A first determining module 405 is used to determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image;

[0166] A transformation module 406, configured to transform the color diffusion tensor image according to the affine transformation matrix to obtain a target color diffusion tensor image;

[0167] The second determination module 407 is used to determine a plurality of target layers that meet a preset condition in the target color diffusion tensor map; the Z-axis coordinate values ​​of a plurality of voxels included in any target layer are the same;

[0168] The traversal module 408 is used to traverse a plurality of voxel pairs related to the first hemisphere in each target layer in the same traversal manner; the plurality of target layers include the first target layer; the traversal manner includes: determining a Y-axis coordinate value range corresponding to the first target layer and an X-axis coordinate value range of the first target layer related to the first hemisphere; traversing a plurality of target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing a plurality of voxel pairs corresponding to each target Y-axis coordinate value in the plurality of target Y-axis coordinate values ​​within the area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the plurality of voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first hemisphere is the left hemisphere or the right hemisphere;

[0169] The third determination module 409 is used to determine, for each voxel pair obtained through traversal, a first candidate interest group and a second candidate interest group corresponding to the voxel pair in the target layer where the voxel pair is located; the first candidate interest group includes: a joint fiber bundle candidate interest group centered on the first voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the second voxel in the voxel pair; the second candidate interest group includes: a joint fiber bundle candidate interest group centered on the second voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest group and the projected fiber bundle candidate interest group are preset shape areas with preset sizes;

[0170] A fourth determination module 410 is used to determine the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI in each candidate ROI combination according to the green value and the blue value of the voxel in the target color diffusion tensor image;

[0171] The fifth determination module 411 is used to determine the combined fiber bundle ROI and the projected fiber bundle ROI according to the candidate ROI combination with the maximum sum of the average green value in the combined fiber bundle candidate ROI and the average blue value in the projected fiber bundle candidate ROI.

[0172] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor images can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor image is transformed to complete the direction correction of the brain image in the color diffusion tensor image, so as to avoid the tilt of the brain image in the color diffusion tensor image from affecting the subsequent analysis process.

[0173] In addition, in this scheme, multiple voxel pairs that meet the requirements are first located by traversal, and the requirements are: the Z-axis coordinate values ​​of the two voxels in the same voxel pair are equal, and the Y-axis coordinate values ​​of the two voxels are equal. Then, combined with the preset shape under the preset size, the first candidate interest combination and the second candidate interest combination corresponding to each voxel pair are automatically determined; the first candidate interest combination includes: the joint fiber bundle candidate interest area centered on the first voxel in the voxel pair and the projected fiber bundle candidate interest area centered on the second voxel in the voxel pair; the second candidate interest combination includes: the joint fiber bundle candidate interest area centered on the second voxel in the voxel pair and the projected fiber bundle candidate interest area centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest area and the projected fiber bundle candidate interest area are preset shape areas under the preset size. Finally, because each voxel in the color diffusion tensor map corresponds to three color values: red value, green value and blue value, where red represents the diffusivity of the voxel along the X-axis direction; green represents the diffusivity of the voxel along the Y-axis direction; and blue represents the diffusivity of the voxel along the Z-axis direction. The joint cellulose is along the Y-axis direction, and the projected cellulose is along the Z-axis direction. Therefore, according to the green value and blue value of the voxel in the target color diffusion tensor map, the average green value in the joint fiber bundle candidate region of interest and the average blue value in the projected fiber bundle candidate region of interest in each candidate region of interest combination are determined; according to the candidate region of interest combination with the largest sum of the average green value in the joint fiber bundle candidate region of interest and the average blue value in the projected fiber bundle candidate region of interest, the joint fiber bundle region of interest and the projected fiber bundle region of interest are automatically determined. The search scheme provided in the embodiment of the present application can quickly and accurately automatically determine the joint fiber bundle region of interest and the projected fiber bundle region of interest.

[0174] Optionally, the combined fiber bundle candidate region of interest and the projected fiber bundle candidate region of interest are circular areas with preset diameters.

[0175] Optionally, the preset conditions include: a preset Z-axis coordinate value range.

[0176] Optionally, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0177] Optionally, a resolution of the standard fractional anisotropy image is greater than a resolution of the fractional anisotropy image.

[0178] It should be noted here that: the device provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0179] Figure 5 FIG. 1 shows a structural block diagram of a data processing device provided by an embodiment of the present application. Figure 5 As shown, the device comprises:

[0180] A first acquisition module 501 is used to acquire an original brain image based on diffusion tensor imaging DTI technology;

[0181] A preprocessing module 502 is used to preprocess the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing;

[0182] A generating module 503, for generating a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image;

[0183] The second acquisition module 504 is used to acquire a preset standard anisotropy fractional image;

[0184] A first determination module 505 is used to determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image;

[0185] A transformation module 506, configured to transform the color diffusion tensor image according to the affine transformation matrix to obtain a target color diffusion tensor image;

[0186] The second determination module 507 is used to determine a plurality of target layers that meet a preset condition in the target color diffusion tensor map; the Z-axis coordinate values ​​of a plurality of voxels included in any target layer are the same;

[0187] The traversal module 508 is used to traverse multiple voxel pairs related to the first hemisphere in each target layer in the same traversal manner; the multiple target layers include the first target layer; the traversal manner includes: determining the Y-axis coordinate value range corresponding to the first target layer and the X-axis coordinate value range of the first target layer related to the first hemisphere; traversing multiple target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing multiple voxel pairs corresponding to each target Y-axis coordinate value in the multiple target Y-axis coordinate values ​​within the area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the multiple voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first hemisphere is the left hemisphere or the right hemisphere;

[0188] The third determination module 509 is used to determine, for each voxel pair obtained through traversal, a first candidate interest group and a second candidate interest group corresponding to the voxel pair in the target layer where the voxel pair is located; the first candidate interest group includes: a joint fiber bundle candidate interest group centered on the first voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the second voxel in the voxel pair; the second candidate interest group includes: a joint fiber bundle candidate interest group centered on the second voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest group and the projected fiber bundle candidate interest group are preset shape areas with preset sizes;

[0189] A fourth determination module 510 is used to determine the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI in each candidate ROI combination according to the green value and the blue value of the voxel in the target color diffusion tensor image;

[0190] A fifth determination module 511 is used to determine a combined fiber bundle ROI and a projected fiber bundle ROI according to a candidate ROI combination with the largest sum of the average green value in the combined fiber bundle candidate ROI and the average blue value in the projected fiber bundle candidate ROI;

[0191] The sixth determination module 512 is used to determine the diffusion tensor imaging analysis index along the perivascular space according to the tensors of the voxels in the joint fiber bundle ROI and the projected fiber bundle ROI in the target color diffusion tensor image.

[0192] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor maps can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor map is transformed to complete the direction correction of the brain image in the color diffusion tensor map.

[0193] In addition, in this scheme, multiple voxel pairs that meet the requirements are first located by traversal. Then, combined with the preset shape under the preset size, the first candidate interest combination and the second candidate interest combination for each voxel pair are automatically determined; the first candidate interest combination includes: the joint fiber bundle candidate interest area centered on the first voxel in the voxel pair and the projection fiber bundle candidate interest area centered on the second voxel in the voxel pair; the second candidate interest combination includes: the joint fiber bundle candidate interest area centered on the second voxel in the voxel pair and the projection fiber bundle candidate interest area centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest area and the projection fiber bundle candidate interest area are preset shape areas under the preset size. Finally, because in the color diffusion tensor map, each voxel corresponds to three color values: red value, green value and blue value, where red represents the diffusion corresponding to the voxel along the X-axis direction; green represents the diffusion corresponding to the voxel along the Y-axis direction; blue represents the diffusion corresponding to the voxel along the Z-axis direction. Among them, the joint cellulose is along the Y-axis direction, and the projected cellulose is along the Z-axis direction, so according to the green value and blue value of the voxel in the target color diffusion tensor map, the average green value in the joint fiber bundle candidate area of ​​interest and the average blue value in the projected fiber bundle candidate area of ​​interest in each candidate area of ​​interest combination are determined; according to the candidate area of ​​interest combination with the largest sum of the average green value in the joint fiber bundle candidate area of ​​interest and the average blue value in the projected fiber bundle candidate area of ​​interest, the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest are automatically determined. The search scheme provided by the embodiment of the present application can automatically and quickly determine the joint fiber bundle area of ​​interest and the projected fiber bundle area of ​​interest, which provides an accurate data basis for the subsequent diffusion tensor imaging analysis along the perivascular space, so that the diffusion tensor imaging analysis index finally determined along the perivascular space has a higher reliability.

[0194] Optionally, the combined fiber bundle candidate region of interest and the projected fiber bundle candidate region of interest are circular areas with preset diameters.

[0195] Optionally, the preset conditions include: a preset Z-axis coordinate value range.

[0196] Optionally, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0197] Optionally, the resolution of the standard anisotropy fractional image is greater than the resolution of the anisotropy fractional image. It should be noted here that the device provided in the above embodiment can implement the technical solutions described in the above method embodiments, and the specific implementation principles of the above modules can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0198] Figure 6 FIG. 1 shows a structural block diagram of a data processing device provided by an embodiment of the present application. Figure 6 As shown, the device comprises:

[0199] A first acquisition module 601 is used to acquire an original brain image based on diffusion tensor imaging DTI technology;

[0200] A preprocessing module 602 is used to preprocess the original brain image to obtain a preprocessed brain image; the preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing;

[0201] A generating module 603, for generating a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image;

[0202] The second acquisition module 604 is used to acquire a preset standard anisotropy fractional image;

[0203] A first determination module 605 is used to determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image;

[0204] A transformation module 606, configured to transform the color diffusion tensor image according to the affine transformation matrix to obtain a target color diffusion tensor image;

[0205] A second determination module 607 is used to determine the joint fiber bundle region of interest and the projection fiber bundle region of interest in the target color diffusion tensor image;

[0206] The third determination module 608 is used to determine the diffusion tensor imaging analysis index along the perivascular space according to the tensor of the voxels in the joint fiber bundle ROI and the projected fiber bundle ROI in the target color diffusion tensor image.

[0207] In the technical solution provided in the embodiment of the present application, the original brain image obtained based on the diffusion tensor imaging DTI technology is pre-processed by noise reduction, artifact removal and / or brain tissue extraction, so that the accuracy of subsequent generation of anisotropy fractional images and color diffusion tensor maps can be improved. According to the affine transformation matrix required to transform the anisotropy fractional image into a standard anisotropy fractional image, the color diffusion tensor map is transformed to complete the direction correction of the brain image in the color diffusion tensor map, providing an accurate data basis for the subsequent diffusion tensor imaging analysis of the perivascular space, so that the final determined diffusion tensor imaging analysis index of the perivascular space has a higher accuracy.

[0208] Optionally, the left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

[0209] Optionally, a resolution of the standard fractional anisotropy image is greater than a resolution of the fractional anisotropy image.

[0210] It should be noted here that: the device provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules can refer to the corresponding contents in the above method embodiments, which will not be repeated here.

[0211] Figure 7 FIG. 1 is a schematic diagram showing the structure of an electronic device provided by an embodiment of the present application. Figure 7 As shown, the electronic device includes a memory 1101 and a processor 1102. The memory 1101 can be configured to store various other data to support operations on the electronic device. Examples of these data include instructions for any application or method for operating on the electronic device. The memory 1101 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0212] The memory 1101 is used to store programs;

[0213] The processor 1102 is coupled to the memory 1101 and is used to execute the program stored in the memory 1101 to implement the search method and data processing method provided by the above-mentioned method embodiments.

[0214] Further, if Figure 7As shown, the electronic device also includes: a communication component 1103, a display 1104, a power component 1105, an audio component 1106 and other components. Figure 7 Only some components are shown schematically, which does not mean that the electronic device only includes Figure 7 Components shown.

[0215] Accordingly, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the steps or functions of the search method and data processing method provided in the above-mentioned method embodiments.

[0216] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0217] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data processing method, characterized in that: include: Obtain original brain images based on diffusion tensor imaging (DTI) technology; Preprocessing the original brain image to obtain a preprocessed brain image; generating a fractional anisotropy image and a color diffusion tensor image according to the preprocessed brain image; Get the preset standard anisotropy fractional image; Determining an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image; transforming the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map; Determining a plurality of target layers that meet preset conditions in the target color diffusion tensor map; the Z-axis coordinate values ​​of a plurality of voxels included in any target layer are the same; In the same traversal method, multiple voxel pairs related to the first hemisphere in each target layer are traversed; The multiple target layers include a first target layer; the traversal method includes: determining a Y-axis coordinate value range corresponding to the first target layer and an X-axis coordinate value range of the first target layer related to the first hemisphere; traversing multiple target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing multiple voxel pairs corresponding to each target Y-axis coordinate value in the multiple target Y-axis coordinate values ​​within an area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the multiple voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first hemisphere is the left hemisphere or the right hemisphere; For each voxel pair obtained through traversal, a first candidate interest group and a second candidate interest group corresponding to the voxel pair are determined in the target layer where the voxel pair is located; the first candidate interest group includes: a joint fiber bundle candidate interest group centered on the first voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the second voxel in the voxel pair; the second candidate interest group includes: a joint fiber bundle candidate interest group centered on the second voxel in the voxel pair and a projected fiber bundle candidate interest group centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest group and the projected fiber bundle candidate interest group are preset shape areas with preset sizes; Determine, according to the green value and the blue value of the voxel in the target color diffusion tensor image, the average green value in the joint fiber bundle candidate region of interest and the average blue value in the projection fiber bundle candidate region of interest in each candidate region of interest combination; According to the candidate region of interest combination with the largest sum of the average green value in the candidate region of interest of the joint fiber bundle and the average blue value in the candidate region of interest of the projection fiber bundle, the joint fiber bundle region of interest and the projection fiber bundle region of interest are determined; According to the tensors of the voxels in the combined fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image, the diffusion tensor imaging analysis index along the perivascular space is determined.

2. The method according to claim 1, characterized in that The combined fiber bundle candidate region of interest and the projected fiber bundle candidate region of interest are circular areas with preset diameters.

3. The method according to claim 1, characterized in that The preset conditions include: a preset Z-axis coordinate value range.

4. The method according to any one of claims 1 to 3, characterized in that The left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

5. The method according to any one of claims 1 to 3, characterized in that The resolution of the standard fractional anisotropy image is greater than the resolution of the fractional anisotropy image.

6. A data processing device, characterized in that: include: The first acquisition module is used to acquire the original brain image based on the diffusion tensor imaging DTI technology; A preprocessing module, used for preprocessing the original brain image to obtain a preprocessed brain image; The preprocessing includes: one or more of: noise reduction processing, artifact removal processing, and brain tissue extraction processing; A generating module, used for generating an anisotropy fractional image and a color diffusion tensor image according to the preprocessed brain image; A second acquisition module is used to acquire a preset standard anisotropy fractional image; A first determining module, configured to determine an affine transformation matrix required to transform the anisotropy fractional image into the standard anisotropy fractional image; A transformation module, used for transforming the color diffusion tensor map according to the affine transformation matrix to obtain a target color diffusion tensor map; A second determination module is used to determine a plurality of target layers that meet a preset condition in the target color diffusion tensor map; the Z-axis coordinate values ​​of a plurality of voxels included in any target layer are the same; A traversal module, used for traversing multiple voxel pairs related to the first half of the brain in each target layer in the same traversal mode; the multiple target layers include the first target layer; the traversal mode includes: determining the Y-axis coordinate value range corresponding to the first target layer and the X-axis coordinate value range of the first target layer related to the first half of the brain; traversing multiple target Y-axis coordinate values ​​within the Y-axis coordinate value range; traversing multiple voxel pairs corresponding to each target Y-axis coordinate value in the multiple target Y-axis coordinate values ​​within the area defined by the X-axis coordinate value range in the first target layer; the Y-axis coordinate value of each voxel in the multiple voxel pairs corresponding to each target Y-axis coordinate value is the target Y-axis coordinate value; the first half of the brain is the left half of the brain or the right half of the brain; The third determination module is used to determine, for each voxel pair obtained through traversal, a first candidate interest group and a second candidate interest group corresponding to the voxel pair in the target layer where the voxel pair is located; the first candidate interest group includes: a joint fiber bundle candidate interest group centered on the first voxel in the voxel pair and a projection fiber bundle candidate interest group centered on the second voxel in the voxel pair; the second candidate interest group includes: a joint fiber bundle candidate interest group centered on the second voxel in the voxel pair and a projection fiber bundle candidate interest group centered on the first voxel in the voxel pair; the joint fiber bundle candidate interest group and the projection fiber bundle candidate interest group are preset shape areas with preset sizes; A fourth determination module is used to determine the average green value in the joint fiber bundle candidate ROI and the average blue value in the projection fiber bundle candidate ROI in each candidate ROI combination according to the green value and the blue value of the voxel in the target color diffusion tensor map; A fifth determination module is used to determine the combined fiber bundle region of interest and the projected fiber bundle region of interest according to the candidate region of interest combination with the largest sum of the average green value in the combined fiber bundle candidate region of interest and the average blue value in the projected fiber bundle candidate region of interest; The sixth determination module is used to determine the diffusion tensor imaging analysis index along the perivascular space according to the tensors of the voxels in the joint fiber bundle region of interest and the projected fiber bundle region of interest in the target color diffusion tensor image.

7. The device according to claim 6, characterized in that The combined fiber bundle candidate region of interest and the projected fiber bundle candidate region of interest are circular areas with preset diameters.

8. The device according to claim 6 or 7, characterized in that The left-right direction of the brain image in the standard anisotropy fractional image is parallel to the X-axis of the standard anisotropy fractional image, the front-back direction is parallel to the Y-axis of the standard anisotropy fractional image, and the up-down direction is parallel to the Z-axis of the standard anisotropy fractional image.

9. An electronic device, characterized in that: include: A memory and a processor, wherein The memory is used to store programs; The processor is coupled to the memory, and is configured to execute the program stored in the memory to implement the method according to any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a computer, the method according to any one of claims 1 to 5 can be implemented.

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