Method and device for determining brain pathogenic area, equipment and medium

Abstract

The invention relates to a brain pathogenic area determination method and device, equipment and a medium. Acquiring a three-dimensional magnetic resonance T1 weighted image and diffusion spectrum imaging data of the brain of the subject; determining a brain structure model based on the weighted image; determining surface light white matter information based on the imaging data and spatial position information of surface light white matter in the brain structure model, and determining a fiber density map based on the imaging data; registering the brain structure models of the two types of users, registering the surface light white matter information of the two types of users, and determining two types of registration results; on the basis of the registration result and a fiber density map, obtaining fiber density values corresponding to all the surface light alphabean plastid points of the two types of users from a brain structure model; and according to the fiber density value and a reference density threshold, positioning a pathogenic area in the brain structure model of the first type of users. Therefore, a quantitative fiber imaging technology, a subcortical surface-based analysis technology and a reference value are combined, and the positioning precision of the brain pathogenic area is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of positioning brain pathogenic areas, and in particular to a method, device, equipment and medium for determining brain pathogenic areas. Background Art

[0002] In recent years, with the complexity of the environment and the increase of life pressure, more and more people have developed various brain diseases. Accurate positioning of the pathogenic areas of the brain is a prerequisite for dealing with brain disease problems.

[0003] In related technologies, diffusion tensor imaging (DTI) is generally used to collect the average diffusion direction in each voxel, and then determine the pathological area of ​​the brain based on this, and then determine the pathological area of ​​the brain based on the tracking results. Among them, the determination tracking method can better analyze the path of the fiber, but it cannot analyze the morphological characteristics such as the crossing within the voxel; the probability tracking method mainly relies on the probability model for mathematical deduction, which is derived from the algorithm rather than directly collected.

[0004] Obviously, the existing methods can only roughly qualitatively analyze the pathogenic areas of the brain, and there are certain positioning errors, which need to be improved. Summary of the invention

[0005] In order to solve the above technical problems, the present disclosure provides a method, device, equipment and medium for determining a brain pathogenic area.

[0006] In a first aspect, the present disclosure provides a method for determining a brain pathogenic region, the method comprising:

[0007] Acquire three-dimensional T1 magnetic resonance weighted images and diffusion spectrum imaging data of the subject's brain;

[0008] Determining a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter;

[0009] Determining superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determining a fiber density map based on the diffusion spectrum imaging data;

[0010] Registering the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and registering the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and determining a first registration result of the brain structure model and a second registration result of the superficial white matter information;

[0011] Based on the first registration result, the second registration result and the fiber density map, obtaining a fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model, wherein the fiber density value includes a first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and a second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user;

[0012] According to the first fiber density value, the second fiber density value and a reference density threshold corresponding to a preset pathogenic type, a target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first category of users.

[0013] In a second aspect, the present disclosure provides a device for determining a brain pathogenic region, the device comprising:

[0014] A first acquisition module is used to acquire three-dimensional T1 magnetic resonance weighted images and diffusion spectrum imaging data of the subject's brain;

[0015] A first determination module is used to determine a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter;

[0016] A second determination module is used to determine the superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determine a fiber density map based on the diffusion spectrum imaging data;

[0017] A registration module, used to register the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and to register the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and to determine a first registration result of the brain structure model and a second registration result of the superficial white matter information;

[0018] A second acquisition module is used to acquire a fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model based on the first registration result, the second registration result and the fiber density map, wherein the fiber density value includes a first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and a second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user;

[0019] The third determination module is used to determine the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of users according to the first fiber density value, the second fiber density value and a reference density threshold corresponding to the preset pathogenic type.

[0020] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the device comprising:

[0021] one or more processors;

[0022] a storage device for storing one or more programs,

[0023] When one or more programs are executed by one or more processors, the one or more processors implement the method provided in the first aspect.

[0024] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method provided in the first aspect is implemented.

[0025] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0026] The present invention discloses a method, device, equipment and medium for determining a brain pathogenic area, which obtains a three-dimensional T1 magnetic resonance weighted image and diffusion spectrum imaging data of a subject's brain; determines a brain structure model of the subject's brain based on the three-dimensional magnetic resonance T1 weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter; determines superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determines a fiber density map based on the diffusion spectrum imaging data; aligns the brain structure model of a first type of user among the subjects with the brain structure model of a second type of user among the subjects, and aligns the superficial white matter information of the first type of user among the subjects with the brain structure model of the second type of user among the subjects; The superficial white matter information of the second type of users is registered to determine the first registration result of the brain structure model and the second registration result of the superficial white matter information; based on the first registration result, the second registration result and the fiber density map, the fiber density value corresponding to each voxel point of the superficial white matter is obtained from the brain structure model, wherein the fiber density value includes the first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of users and the second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of users; according to the first fiber density value, the second fiber density value and the reference density threshold corresponding to the preset pathogenic type, the target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first type of users. Thus, the quantitative fiber imaging technology and the subcortical surface-based analysis technology are combined to quantitatively track the fiber density values ​​corresponding to each voxel point of the superficial white matter, and based on the fiber density values ​​corresponding to each voxel point of the superficial white matter and the reference value, the target pathogenic area of ​​the preset pathogenic type is quantitatively determined, thereby improving the positioning accuracy of the brain pathogenic area. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0029] Figure 1 A schematic diagram of a flow chart of a method for determining a brain pathogenic region provided in an embodiment of the present disclosure;

[0030] Figure 2 A schematic diagram of the structure of a device for determining a brain pathogenic area provided by an embodiment of the present disclosure;

[0031] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0034] In the study of brain tissue cases, the content of superficial white matter fibers became weak in the diseased areas of cortical dysplasia, among which U-shaped fibers were small superficial white matter fibers connecting adjacent gyri.

[0035] In addition, related studies have projected the collected fiber density map (Track Density Imaging, TDI) to the superficial white matter 2mm below the gray matter of the brain's pathogenic area, and observed a decrease in fiber density values. By comparing the characteristics of the fibers in the brain's pathogenic area between patients and healthy people, it was confirmed that the decrease in the fiber density value of the superficial white matter has a certain correlation with the brain's pathogenic area.

[0036] In order to solve the above problems and effectively apply the above research results, the following is combined with Figure 1The method for determining the brain pathogenic area provided by the embodiment of the present disclosure is described. In the embodiment of the present disclosure, the method for determining the brain pathogenic area can be executed by an electronic device or a server. The electronic device may include a device with a communication function such as a tablet computer, a desktop computer, a laptop computer, or a device simulated by a virtual machine or a simulator. The server may be a cloud server or a server cluster.

[0037] Figure 1 A schematic flow chart of a method for determining a brain pathogenic area provided in an embodiment of the present disclosure is shown.

[0038] like Figure 1 As shown, the method for determining the brain pathogenic area may include the following steps.

[0039] S110, obtaining three-dimensional magnetic resonance T1-weighted images and diffusion spectrum imaging data of the subject's brain.

[0040] Before locating the disease-causing area in the brain, the user starts the magnetic resonance device to acquire a three-dimensional magnetic resonance T1-weighted image of the subject's brain, and at the same time, controls the magnetic resonance device to acquire diffusion spectrum imaging data of the subject's brain in diffusion spectrum imaging (DSI) mode, so that the electronic device can obtain three-dimensional magnetic resonance T1-weighted images and diffusion spectrum imaging data.

[0041] The subjects are research objects composed of multiple types of test users, specifically including the first type of users and the second type of users. The first type of users refers to sick users with pathogenic areas in the brain, and the second type of users refers to healthy users without pathogenic areas in the brain.

[0042] Among them, three-dimensional magnetic resonance T1 weighted image refers to 3D T1 magnetic resonance weighted data, which is a data collected by imaging technology based on T1 relaxation time and is used to display anatomical structure and pathological changes.

[0043] The diffusion spectrum imaging data is DSI data of the subject's brain collected by the magnetic resonance imaging device in the diffusion spectrum imaging mode.

[0044] S120. Determine a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter.

[0045] In this embodiment, the electronic device uses an open source software toolkit (such as FreeSurfer technology) to perform three-dimensional reconstruction on the three-dimensional magnetic resonance T1 weighted image to obtain a brain structure model including the spatial position information of the superficial white matter between the gray matter and the white matter.

[0046] Among them, the specific implementation method of S120 includes but is not limited to the following method: performing three-dimensional reconstruction on the three-dimensional magnetic resonance T1 weighted image to obtain a brain structure reconstruction model with gray matter and white matter segmented as the brain structure model of the subject's brain.

[0047] The brain structure model is a three-dimensional model including white matter and gray matter obtained by three-dimensional reconstruction of three-dimensional magnetic resonance weighted images or brain structure reconstruction, and the brain structure model also includes spatial position information of superficial white matter between gray matter and white matter.

[0048] The superficial white matter refers to the white matter 2 mm inward from the boundary between the white matter and the gray matter in the brain structure model.

[0049] In this step, the electronic device can also use specific functional software (such as the flirt command of FSL software) to align the diffusion spectrum imaging data of the same subject to the three-dimensional T1 magnetic resonance weighted image to achieve six-degree-of-freedom alignment between individual intra-modalities, so as to facilitate the subsequent use of the alignment results to continue to align between different subjects.

[0050] S130, determining superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determining a fiber density map based on the diffusion spectrum imaging data.

[0051] In this embodiment, the specific implementation method of "determining the superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter" in S130 includes but is not limited to the following methods: performing fiber bundle tracking processing on the diffusion spectrum imaging data to determine the fiber bundle information of the superficial white matter passing through the subject's brain; determining the superficial white matter information based on the spatial position information of the superficial white matter and the fiber bundle information of the superficial white matter.

[0052] Specifically, after the electronic device performs fiber bundle tracking processing on the diffusion spectrum imaging data, it can obtain the fiber bundle information of the subject's brain, which is a kind of image information, and extract the fiber bundle information of the superficial white matter from the fiber bundle information of the subject's brain, and then associate the spatial position information of the superficial white matter with the fiber bundle information of the superficial white matter, which essentially associates the position of each voxel point of the superficial white matter with the fiber bundle image value of the superficial white matter, thereby obtaining the superficial white matter information.

[0053] In this embodiment, the specific implementation method of "determining the fiber density map based on the diffusion spectrum imaging data" in S130 includes but is not limited to the following methods: performing fiber bundle tracking processing on the diffusion spectrum imaging data to obtain fiber bundle information of different voxel points passing through the subject's brain; obtaining the number of fiber bundles corresponding to different voxel points from the fiber bundle information of different voxel points, and generating a fiber density map according to the number of fiber bundles corresponding to different voxel points.

[0054] Specifically, after the electronic device performs fiber bundle tracking processing on the diffusion spectrum imaging data, it can obtain the fiber bundle information passing through different voxel points of the subject's brain. The fiber bundle information of different voxel points of the subject's brain is a kind of image information. Then, the number of fiber bundles corresponding to different voxel points is calculated from the fiber bundle information of different voxel points. Then, the image formed by using the number of fiber bundles as the voxel value is the fiber density map. In other words, by performing fiber tracking on the diffusion spectrum image, the fibers in the diffusion spectrum image are projected onto a three-dimensional grid to form a fiber density map.

[0055] Among them, the fiber density map is a collection of fiber densities collected using quantitative fiber imaging technology. That is to say, fiber tracking is performed on the diffusion spectrum imaging data collected using DSI technology, and then the fiber bundle information in the diffusion spectrum imaging data is projected onto a three-dimensional grid to obtain a fiber density map.

[0056] S140, aligning the brain structure model of the first type of users among the subjects with the brain structure model of the second type of users among the subjects, and aligning the superficial white matter information of the first type of users among the subjects with the superficial white matter information of the second type of users among the subjects, to determine a first alignment result of the brain structure model and a second alignment result of the superficial white matter information.

[0057] In order to facilitate the observation of the difference in brain information between the first type of users and the second type of users, it is necessary to align the brain structure models of the two types of users, as well as the superficial white matter information of the two types of users, so as to achieve a unified brain structure space for diseased users and healthy users.

[0058] Among them, the specific implementation method of S140 includes but is not limited to the following method: based on the surface and volume registration method, the brain structure model of the first type of user is registered with the brain structure model of the second type of user to obtain a first registration result of the brain structure model, and the superficial white matter information of the first type of user is registered with the superficial white matter information of the second type of user to obtain a second registration result of the superficial white matter information.

[0059] Specifically, the electronic device uses a combined volumetric and surface registration (CVS) method to register the brain structure models of the two types of users, and to register the superficial white matter information of the two types of users, so that the brain structure models of the two types of users are aligned, and the superficial white matter information of the two types of users is aligned, thereby obtaining the first registration result of the brain structure model of the two types of users and the second registration result of the superficial white matter information of the two types of users. It should be noted that for the same subject, six degrees of freedom registration between individual intramodalities is achieved by registering the diffusion spectrum imaging data to the three-dimensional T1 magnetic resonance weighted image. In addition, since the superficial white matter information of the subject's brain is determined based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, the brain structure model contains the spatial position information of the superficial white matter. Therefore, by copying the individual modal space of the subject, it is possible to register the superficial white matter information of the two types of subjects to obtain the second registration result.

[0060] In this way, the volume (voxel level) and surface (cortical surface model) information are combined to align the brain structure models and superficial white matter information of the two types of users, eliminating the morphological differences in the brain structure models and the differences in superficial white matter information of the two types of users, thereby improving the accuracy and robustness of the alignment.

[0061] S150. Based on the first registration result, the second registration result and the fiber density map, the fiber density value corresponding to each voxel point of the superficial white matter is obtained from the brain structure model, wherein the fiber density value includes the first fiber density value of each voxel point of the superficial white matter voxel point in the brain structure model of the first type of user and the second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user.

[0062] It can be understood that the first registration result can highlight the difference in brain structure models of the two types of users, and the second registration result can highlight the difference in superficial white matter information of the two types of users. The electronic device combines the first registration result, the second registration result and the fiber density map of the two types of users to obtain the fiber density value of each superficial white matter voxel point from the brain structure model of the two types of users, and further relies on the theoretical basis that the fiber density value of the superficial white matter is correlated with the pathogenic area of ​​the brain to locate the pathogenic area from the brain structure model of the first type of user.

[0063] Among them, the specific implementation method of S150 includes but is not limited to the following methods: aligning the spatial position information of each voxel point in the fiber density map with the spatial position information of each pixel point in the three-dimensional T1 magnetic resonance weighted image to obtain a third alignment result; based on the first alignment result, the second alignment result and the third alignment result, adding the fiber density value of each voxel point in the fiber density map to the corresponding voxel point of the brain structure model; based on the spatial position information of each voxel point in the brain structure model, reading the fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model.

[0064] Among them, based on the spatial position information of each voxel point in the brain structure model, the fiber density value corresponding to each voxel point of the superficial white matter is read from the brain structure model. The specific implementation method includes but is not limited to the following method: converting the brain structure model with added fiber density value from the individual brain structure space of the subject to the same standard brain structure space; in the same standard brain structure space, based on the spatial position information of each voxel point in the brain structure model, reading the fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model.

[0065] Specifically, during the execution of S150, or before the execution of S150, the electronic device aligns the fiber density map to the space where the three-dimensional T1 magnetic resonance weighted image is located (i.e., the 3DT1 space), specifically, the spatial position information of each voxel point in the fiber density map is aligned with the spatial position information of each pixel point in the three-dimensional magnetic resonance weighted image to obtain a third registration result, so that the spatial position information of each voxel in the fiber density map is aligned with the spatial position information of each pixel point in the three-dimensional magnetic resonance weighted image; then, since the first registration result represents the alignment result of the brain structure models of the two types of users, and the second registration result represents the alignment result of the superficial white matter information of the two types of users, the electronic device can add the fiber density value of each voxel point in the fiber density map to the corresponding voxel point of the brain structure model based on the first registration result, the second registration result and the third registration result, so that the fiber density The fiber density value of each voxel point in the figure is interpolated to the corresponding voxel of the brain structure model; finally, the registration process of the spatial position information of each voxel in the fiber density map and the spatial position information of each pixel point in the three-dimensional magnetic resonance T1 weighted image is performed in the individual brain structure space of the two types of users themselves, and the process of adding the fiber density value of each voxel point in the fiber density map to the corresponding voxel point of the brain structure model is also performed in the individual brain structure space of the two types of users themselves, however, the process of reading the fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model needs to be implemented in the same standard brain structure space. Therefore, the electronic device needs to first convert the brain structure model with the added fiber density value from the individual brain structure space of the two types of users themselves to the same standard brain structure space, and then read the fiber density value corresponding to each voxel point of the superficial white matter in the brain structure model in the same standard brain structure space.

[0066] In this way, relying on multiple registration results, the fiber density values ​​of each voxel point in the fiber density map are interpolated to the corresponding voxel point of the brain structure model, so that each voxel point of the brain structure model is associated with a corresponding fiber density value, and then the fiber density information of the superficial white matter at the gray-white matter junction is accurately extracted, so as to further locate the pathogenic area in the brain structure model based on the fiber density information of the gray-white matter junction. In addition, because this process projects the subcortical structure data of the two groups of people into the same standard brain structure space, it can specifically extract the density signal of the superficial white matter under the premise of fully eliminating the morphological differences between subjects.

[0067] S160. Determine a target pathogenic area of ​​a preset pathogenic type from the brain structure model of the first category of users according to the first fiber density value, the second fiber density value, and a reference density threshold corresponding to the preset pathogenic type.

[0068] In this embodiment, after the electronic device reads the first fiber density value of each voxel point in the superficial white matter in the brain structure model of the first type of user and the first fiber density value of each voxel point in the superficial white matter in the brain structure model of the second type of user, the first fiber density value and the second fiber density value are compared, and combined with the reference density threshold corresponding to the preset pathogenic type, the pathogenic area is located from the brain structure model of the first type of user, so as to determine the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of user.

[0069] The preset pathogenic type refers to the common brain disease type of the subject. Optionally, the preset pathogenic type includes any one of the following: epilepsy type, Parkinson's type, depression type, schizophrenia type, autism type, anxiety type, brain tumor type.

[0070] Among them, the reference density threshold refers to the normal fiber density range in which the preset pathogenic type occurs.

[0071] Among them, the specific implementation method of S160 includes but is not limited to the following methods: calculating the fiber density average value and fiber density standard deviation of the second fiber density value; determining the fiber density index value of the brain of the first type of user based on the first fiber density value, the fiber density average value and the fiber density standard deviation; determining the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of user according to the fiber density index value and the reference density threshold corresponding to the preset pathogenic type.

[0072] Among them, according to the fiber density index value and the reference density threshold corresponding to the preset pathogenic type, the target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first category of users. The specific implementation method includes but is not limited to the following method: obtaining the spatial position information of the target voxel point whose fiber density index value is less than the reference density threshold from the brain structure model of the first category of users; performing regional construction on the spatial position information of the target voxel point, and determining the target pathogenic area of ​​the preset pathogenic type in the brain structure model of the first category of users.

[0073] Specifically, since the second category of users are healthy users without pathogenic areas in their brains, the second fiber density value corresponding to the second category of users can be used as a reference, and the electronic device uses a Z-transformation or a t-test method of a single patient to a healthy group to calculate how many times the standard deviation the first category of users deviates from the mean of the second category of users. Specifically, the electronic device first calculates the fiber density average value and fiber density standard deviation of the second fiber density value, then calculates the difference between the first fiber density value and the fiber density average value, and divides the difference by the fiber density standard deviation to obtain the fiber density index value of the brain of the first category of users. Finally, for each voxel point on the brain structure model of the first category of users, the fiber density index value corresponding to the first category of users is compared with the reference density threshold of the preset pathogenic type, so as to obtain the spatial position information of the target voxel point whose fiber density index value is less than the reference density threshold from the brain structure model of the first category of users, and then locates the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first category of users by constructing the spatial position information of the target voxel point in a regional manner.

[0074] In this way, after extracting the fiber density value of each superficial white matter voxel point in the brain structure model of the two types of users, a quantitative positioning analysis method is used to accurately determine the area that deviates from the normal in the brain structure model of the first type of users as the target pathogenic area of ​​the preset pathogenic type.

[0075] A method for determining a brain pathogenic area in an embodiment of the present disclosure comprises obtaining a three-dimensional magnetic resonance T1 weighted image and diffusion spectrum imaging data of a subject's brain; determining a brain structure model of the subject's brain based on the three-dimensional magnetic resonance T1 weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter; determining superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determining a fiber density map based on the diffusion spectrum imaging data; registering the brain structure model of a first type of user among the subjects with the brain structure model of a second type of user among the subjects, and registering the superficial white matter information of the first type of user among the subjects with the brain structure model of the second type of user among the subjects; The first registration result of the brain structure model and the second registration result of the superficial white matter information are registered with the superficial white matter information of the user to determine the first registration result of the brain structure model and the second registration result of the superficial white matter information; based on the first registration result, the second registration result and the fiber density map, the fiber density value corresponding to each voxel point of the superficial white matter is obtained from the brain structure model, wherein the fiber density value includes the first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and the second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user; according to the first fiber density value, the second fiber density value and the reference density threshold corresponding to the preset pathogenic type, the target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first type of user. Thus, the quantitative fiber imaging technology and the subcortical surface-based analysis technology are combined to quantitatively track the fiber density values ​​corresponding to each voxel point of the superficial white matter, and based on the fiber density values ​​corresponding to each voxel point of the superficial white matter and the reference value, the target pathogenic area of ​​the preset pathogenic type is quantitatively determined, thereby improving the positioning accuracy of the brain pathogenic area.

[0076] Furthermore, after executing S150, the method further includes: importing the target pathogenic area into the target robot, so that the target robot controls the end effector to operate in the brain of the first type of user based on the target pathogenic area.

[0077] Among them, the target pathogenic area is imported into the target robot, and the specific implementation method includes but is not limited to the following methods: converting the target pathogenic area from the same standard brain structure space where the brain structure model is located to the individual brain structure space corresponding to the first type of user; importing the target pathogenic area in the individual brain structure space into the target robot.

[0078] It should be noted that since the process of obtaining the fiber density value corresponding to each voxel point of the superficial white matter and the process of locating the pathogenic area in the brain structure model in the above process are performed in the same standard brain structure space, however, guiding the robot to operate on the pathogenic area needs to be performed in the individual brain structure space corresponding to the first type of user. For this reason, after determining the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of user, the electronic device needs to first convert the target pathogenic area from the same standard brain structure space where the brain structure model is located to the individual brain structure space corresponding to the first type of user, and then import the target pathogenic area into the target robot in the individual brain structure space corresponding to the first type of user, so that the target robot controls the end effector based on the target pathogenic area to operate in the brain of the first type of user.

[0079] In this way, after quantitatively locating the pathogenic area, the located pathogenic area is introduced into the robot to perform operations accurately, which is conducive to accurately treating the brain disease problems of the sick users.

[0080] The present disclosure also provides a device for determining a brain pathogenic region for implementing the above-mentioned method for determining a brain pathogenic region. Figure 2 In the disclosed embodiment, the device for determining the brain pathogenic area may be an electronic device or a server. The electronic device may include a tablet computer, a desktop computer, a laptop computer, or other device with a communication function, or may include a virtual machine or a device simulated by a simulator. The server may be a cloud server or a server cluster.

[0081] Figure 2 A schematic diagram of the structure of a device for determining a brain pathogenic area provided by an embodiment of the present disclosure is shown.

[0082] like Figure 2 As shown, the device 200 for determining the brain pathogenic region may include:

[0083] A first acquisition module 210 is used to acquire a three-dimensional T1 magnetic resonance weighted image and diffusion spectrum imaging data of the subject's brain;

[0084] A first determination module 220 is used to determine a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter;

[0085] A second determination module 230 is used to determine the superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and to determine a fiber density map based on the diffusion spectrum imaging data;

[0086] A registration module 240 is used to register the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and to register the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and to determine a first registration result of the brain structure model and a second registration result of the superficial white matter information;

[0087] A second acquisition module 250 is used to acquire a fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model based on the first registration result, the second registration result and the fiber density map, wherein the fiber density value includes a first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and a second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user;

[0088] The third determination module 260 is used to determine the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of users according to the first fiber density value, the second fiber density value and the reference density threshold corresponding to the preset pathogenic type.

[0089] The present invention discloses a device for determining a brain pathogenic area, which obtains a three-dimensional T1 magnetic resonance weighted image and diffusion spectrum imaging data of a subject's brain; determines a brain structure model of the subject's brain based on the three-dimensional magnetic resonance T1 weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter; determines superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determines a fiber density map based on the diffusion spectrum imaging data; aligns the brain structure model of a first type of user among the subjects with the brain structure model of a second type of user among the subjects, and aligns the superficial white matter information of the first type of user among the subjects with the brain structure model of the second type of user among the subjects. The first registration result of the brain structure model and the second registration result of the superficial white matter information are registered with the superficial white matter information of the user to determine the first registration result of the brain structure model and the second registration result of the superficial white matter information; based on the first registration result, the second registration result and the fiber density map, the fiber density value corresponding to each voxel point of the superficial white matter is obtained from the brain structure model, wherein the fiber density value includes the first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and the second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user; according to the first fiber density value, the second fiber density value and the reference density threshold corresponding to the preset pathogenic type, the target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first type of user. Thus, the quantitative fiber imaging technology and the subcortical surface-based analysis technology are combined to quantitatively track the fiber density values ​​corresponding to each voxel point of the superficial white matter, and based on the fiber density values ​​corresponding to each voxel point of the superficial white matter and the reference value, the target pathogenic area of ​​the preset pathogenic type is quantitatively determined, thereby improving the positioning accuracy of the brain pathogenic area.

[0090] In some embodiments of the present disclosure, the first determining module 220 includes:

[0091] The three-dimensional reconstruction unit is used to perform three-dimensional reconstruction on the three-dimensional T1 magnetic resonance weighted image to obtain a brain structure reconstruction model with gray matter and white matter segmented as the brain structure model of the subject's brain.

[0092] In some embodiments of the present disclosure, the second determination module 230 includes:

[0093] A first tracking unit is used to perform fiber bundle tracking processing on the diffusion spectrum imaging data to determine fiber bundle information passing through the superficial white matter of the subject's brain;

[0094] The first determining unit is used to determine the superficial white matter information according to the spatial position information of the superficial white matter and the fiber bundle information of the superficial white matter.

[0095] In some embodiments of the present disclosure, the second determination module 230 includes:

[0096] A second tracking unit is used to perform fiber bundle tracking processing on the diffusion spectrum imaging data to obtain fiber bundle information passing through different voxel points of the subject's brain;

[0097] The first generating unit is used to obtain the number of fiber bundles corresponding to different voxel points from the fiber bundle information of the different voxel points, and generate the fiber density map according to the number of fiber bundles corresponding to the different voxel points.

[0098] In some embodiments of the present disclosure, the registration module 240 includes:

[0099] The first registration unit is used to align the brain structure model of the first category of users with the brain structure model of the second category of users based on a surface and volume registration method to obtain a first registration result of the brain structure model, and to align the superficial white matter information of the first category of users with the superficial white matter information of the second category of users to obtain a second registration result of the superficial white matter information.

[0100] In some embodiments of the present disclosure, the second acquisition module 250 includes:

[0101] A third registration unit is used to register the spatial position information of each voxel point in the fiber density map with the spatial position information of each pixel point in the three-dimensional T1 magnetic resonance weighted image to obtain a third registration result;

[0102] an adding unit, configured to add the fiber density value of each voxel point in the fiber density map to the corresponding voxel point of the brain structure model based on the first registration result, the second registration result and the third registration result;

[0103] A reading unit is used to read the fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model based on the spatial position information of each voxel point in the brain structure model.

[0104] In some embodiments of the present disclosure, the reading unit is specifically used to:

[0105] The brain structure model to which the fiber density value is added is converted from the subject's individual brain structure space to the same standard brain structure space;

[0106] In the same standard brain structure space, based on the spatial position information of each voxel point in the brain structure model, the fiber density value corresponding to each voxel point of the superficial white matter is read from the brain structure model.

[0107] In some embodiments of the present disclosure, the third determination module 260 includes:

[0108] A calculation unit, used for calculating a fiber density average value and a fiber density standard deviation of the second fiber density value;

[0109] A second determining unit, configured to determine a fiber density index value of a brain of a first type of user based on the first fiber density value, the fiber density average value, and the fiber density standard deviation;

[0110] The third determination unit is used to determine the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first category of users according to the fiber density index value and the reference density threshold corresponding to the preset pathogenic type.

[0111] In some embodiments of the present disclosure, the third determining unit is specifically configured to:

[0112] Acquire spatial position information of a target voxel point whose fiber density index value is less than the reference density threshold from the brain structure model of the first type of user;

[0113] The spatial position information of the target voxel point is subjected to regional construction, and the target pathogenic area of ​​the preset pathogenic type is determined in the brain structure model of the first type of user.

[0114] In some embodiments of the present disclosure, the device further includes:

[0115] An import module is used to import the target pathogenic area into a target robot, so that the target robot controls an end effector to operate in the brain of the first type of user based on the target pathogenic area.

[0116] In some embodiments of the present disclosure, the import module is specifically used to:

[0117] Converting the target pathogenic region from the same standard brain structure space where the brain structure model is located to the individual brain structure space corresponding to the first type of user;

[0118] The target pathogenic area in the individual's brain structure space is introduced into the target robot.

[0119] In some embodiments of the present disclosure, the preset pathogenicity type includes any one of the following:

[0120] Epilepsy types, Parkinson's types, depression types, schizophrenia types, autism types, anxiety types, brain tumor types.

[0121] It should be noted that Figure 2 The brain pathogenic region determination device 200 shown can be implemented Figure 1 The various steps in the method embodiment shown in the figure are implemented Figure 1 The various processes and effects in the method embodiment shown are not described in detail here.

[0122] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown.

[0123] like Figure 3 As shown, the electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0124] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0125] The memory 302 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 302 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 302 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 302 is a non-volatile solid-state memory. In a particular embodiment, the memory 302 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.

[0126] The processor 301 reads and executes the computer program instructions stored in the memory 302 to perform the steps of the method for determining the brain pathogenic area provided in the embodiment of the present disclosure.

[0127] In one example, the electronic device may further include a transceiver 303 and a bus 304. Figure 3 As shown, the processor 301, the memory 302 and the transceiver 303 are connected via a bus 304 and communicate with each other.

[0128] The bus 304 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a Memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 304 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0129] The following is an embodiment of a computer-readable storage medium provided in an embodiment of the present disclosure. The computer-readable storage medium and the method for determining the brain pathogenic area in the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the computer-readable storage medium, reference can be made to the embodiment of the method for determining the brain pathogenic area in the above-mentioned embodiments.

[0130] This embodiment provides a storage medium containing computer executable instructions. When the computer executable instructions are executed by a computer processor, they are used to perform a method for determining a brain pathogenic region. The method includes:

[0131] Acquire three-dimensional T1 magnetic resonance weighted images and diffusion spectrum imaging data of the subject's brain;

[0132] Determining a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter;

[0133] Determining superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determining a fiber density map based on the diffusion spectrum imaging data;

[0134] Registering the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and registering the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and determining a first registration result of the brain structure model and a second registration result of the superficial white matter information;

[0135] Based on the first registration result, the second registration result and the fiber density map, obtaining a fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model, wherein the fiber density value includes a first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and a second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user;

[0136] According to the first fiber density value, the second fiber density value and a reference density threshold corresponding to a preset pathogenic type, a target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first category of users.

[0137] Of course, the computer executable instructions of a storage medium containing computer executable instructions provided in an embodiment of the present disclosure are not limited to the above method operations, and can also execute related operations in the method for determining the brain pathogenic area provided in any embodiment of the present disclosure.

[0138] Through the above description of the implementation methods, the technical personnel in the relevant field can clearly understand that the present disclosure can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure can be 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 a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions to enable a computer cloud platform (which can be a personal computer, a server, or a network cloud platform, etc.) to execute the method for determining the brain pathogenic area provided by each embodiment of the present disclosure.

[0139] Note that the above are only preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art will understand that the present disclosure is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present disclosure. Therefore, although the present disclosure is described in more detail through the above embodiments, the present disclosure is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present disclosure, and the scope of the present disclosure is determined by the scope of the appended claims.

Claims

1. A method for determining a brain pathogenic area, characterized in that: include: Acquire three-dimensional T1 magnetic resonance weighted images and diffusion spectrum imaging data of the subject's brain; Determining a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter; Determining superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determining a fiber density map based on the diffusion spectrum imaging data; Registering the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and registering the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and determining a first registration result of the brain structure model and a second registration result of the superficial white matter information; Based on the first registration result, the second registration result and the fiber density map, obtaining a fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model, wherein the fiber density value includes a first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and a second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user; According to the first fiber density value, the second fiber density value and a reference density threshold corresponding to a preset pathogenic type, a target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first category of users.

2. The method according to claim 1, characterized in that The step of determining the brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image comprises: The three-dimensional T1 magnetic resonance weighted image is three-dimensionally reconstructed to obtain a brain structure reconstruction model with gray matter and white matter segmented as the brain structure model of the subject's brain.

3. The method according to claim 1, characterized in that: The determining of superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter includes: Performing fiber tract tracking processing on the diffusion spectrum imaging data to determine fiber tract information passing through the superficial white matter of the subject's brain; The superficial white matter information is determined based on the spatial position information of the superficial white matter and the fiber bundle information of the superficial white matter.

4. The method according to claim 1, characterized in that: Determining a fiber density map based on the diffusion spectrum imaging data comprises: Performing fiber bundle tracking processing on the diffusion spectrum imaging data to obtain fiber bundle information passing through different voxel points of the subject's brain; The number of fiber bundles corresponding to different voxel points is obtained from the fiber bundle information of the different voxel points, and the fiber density map is generated according to the number of fiber bundles corresponding to the different voxel points.

5. The method according to claim 1, characterized in that The registering the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and registering the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and determining a first registration result of the brain structure model and a second registration result of the superficial white matter information, includes: Based on the surface and volume registration method, the brain structure model of the first type of users is registered with the brain structure model of the second type of users to obtain a first registration result of the brain structure model, and the superficial white matter information of the first type of users is registered with the superficial white matter information of the second type of users to obtain a second registration result of the superficial white matter information.

6. The method according to claim 1, characterized in that The obtaining, based on the first registration result, the second registration result and the fiber density map, fiber density values ​​corresponding to each voxel point of the superficial white matter from the brain structure model includes: Registering the spatial position information of each voxel point in the fiber density map with the spatial position information of each pixel point in the three-dimensional T1 magnetic resonance weighted image to obtain a third registration result; Based on the first registration result, the second registration result and the third registration result, adding the fiber density value of each voxel point in the fiber density map to the corresponding voxel point of the brain structure model; Based on the spatial position information of each voxel point in the brain structure model, the fiber density value corresponding to each voxel point of the superficial white matter is read from the brain structure model.

7. The method according to claim 6, characterized in that The step of reading the fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model based on the spatial position information of each voxel point in the brain structure model includes: The brain structure model to which the fiber density value is added is converted from the subject's individual brain structure space to the same standard brain structure space; In the same standard brain structure space, based on the spatial position information of each voxel point in the brain structure model, the fiber density value corresponding to each voxel point of the superficial white matter is read from the brain structure model.

8. The method according to claim 1, characterized in that The determining, based on the first fiber density value, the second fiber density value, and a reference density threshold corresponding to a preset pathogenic type, a target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of user includes: Calculate the fiber density average and fiber density standard deviation of the second fiber density value; Determining a fiber density index value of a brain of a first type of user based on the first fiber density value, the fiber density average value, and the fiber density standard deviation; According to the fiber density index value and the reference density threshold corresponding to the preset pathogenic type, a target pathogenic area of ​​the preset pathogenic type is determined from the brain structure model of the first category of users.

9. The method according to claim 8, characterized in that The step of determining the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first category of users according to the fiber density index value and the reference density threshold corresponding to the preset pathogenic type includes: Acquire spatial position information of a target voxel point whose fiber density index value is less than the reference density threshold from the brain structure model of the first type of user; The spatial position information of the target voxel point is subjected to regional construction, and the target pathogenic area of ​​the preset pathogenic type is determined in the brain structure model of the first type of user.

10. The method according to claim 1, characterized in that Also includes: The target pathogenic area is introduced into a target robot, so that the target robot controls an end effector to perform an operation in the brain of the first type of user based on the target pathogenic area.

11. The method according to claim 10, characterized in that The step of introducing the target pathogenic area into the target robot comprises: Converting the target pathogenic region from the same standard brain structure space where the brain structure model is located to the individual brain structure space corresponding to the first type of user; The target pathogenic area in the individual's brain structure space is introduced into the target robot.

12. The method according to any one of claims 1 to 9, characterized in that: The preset pathogenicity type includes any one of the following: Epilepsy types, Parkinson's types, depression types, schizophrenia types, autism types, anxiety types, brain tumor types.

13. A device for determining a brain pathogenic area, characterized in that: include: A first acquisition module is used to acquire a three-dimensional T1 magnetic resonance weighted image and diffusion spectrum imaging data of the subject's brain; A first determination module is used to determine a brain structure model of the subject's brain based on the three-dimensional T1 magnetic resonance weighted image, wherein the brain structure model includes spatial position information of superficial white matter between gray matter and white matter; A second determination module is used to determine the superficial white matter information of the subject's brain based on the diffusion spectrum imaging data and the spatial position information of the superficial white matter, and determine a fiber density map based on the diffusion spectrum imaging data; A registration module, used to register the brain structure model of the first type of user among the subjects with the brain structure model of the second type of user among the subjects, and to register the superficial white matter information of the first type of user among the subjects with the superficial white matter information of the second type of user among the subjects, and to determine a first registration result of the brain structure model and a second registration result of the superficial white matter information; A second acquisition module is used to acquire a fiber density value corresponding to each voxel point of the superficial white matter from the brain structure model based on the first registration result, the second registration result and the fiber density map, wherein the fiber density value includes a first fiber density value of each voxel point of the superficial white matter in the brain structure model of the first type of user and a second fiber density value of each voxel point of the superficial white matter in the brain structure model of the second type of user; The third determination module is used to determine the target pathogenic area of ​​the preset pathogenic type from the brain structure model of the first type of users according to the first fiber density value, the second fiber density value and a reference density threshold corresponding to the preset pathogenic type.

14. An electronic device, characterized in that: include: processor; A memory for storing executable instructions; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the method described in any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the method according to any one of claims 1 to 12.

Images