31 results about "Brain White Matter" patented technology

The invention discloses an Alzheimer's disease early warning method based on white matter lesion omics characteristics, and relates to the field of wisdom medicines.The method comprises the steps that magnetic resonance imaging data of a historical subject in the period from the mild cognitive impairment period to the period before diagnosis of Alzheimer's disease are obtained, and manual labeling of white matter and white matter lesion areas is carried out; a manual annotation data set is obtained; training a deep learning model for white matter lesion recognition based on the manual annotation data set; inputting to-be-identified magnetic resonance imaging data into the deep learning model, and extracting lesion features of the white matter; performing standardization and feature alignment on the extracted lesion features, and inputting the lesion features into a deep clustering model to form clustering results for different white matter lesion feature types; and an early risk assessment model is constructed based on the clustering result and the Alzheimer's disease transformation risk tag corresponding to the clustering result, and the Alzheimer's disease transformation risk level of the subject is output, so that the problems that multiple lesion features are difficult to quantify and details are difficult to identify are solved.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention relates to a heterogeneous human head model construction method and system oriented to multi-physics field simulation, based on real human head T1 weighted magnetic resonance image data, key tissues such as white matter, gray matter and cerebrospinal fluid are accurately segmented, and a three-dimensional geometric model used for generating carrying tissue identification information is researched and developed; converting the model data into a file format conforming to the NASTRAN standard by utilizing an independently developed format conversion program, and completely retaining organization classification information; finally, different tissue areas are automatically recognized through physical field simulation software, corresponding frequency dependent material attributes are distributed, and a finite element model capable of being directly used for simulation is formed. According to the method, seamless integration of the high-precision heterogeneous human head model and the simulation platform is achieved, tedious manual repairing and assignment operation is not needed, the method has the advantages of being high in precision, high in efficiency and good in compatibility, and the authenticity and reliability of biomedical simulation such as transcranial magnetic stimulation and radio frequency coil design can be remarkably improved.

The application discloses a kind of whole brain segmentation method, system, equipment and medium, method includes obtaining CT image data, and standard image data is obtained by preprocessing;Standard image data is input into whole brain segmentation model, and whole brain segmentation result is obtained.Wherein, whole brain segmentation model is obtained based on sample annotation data training, and whole brain segmentation model includes CNN encoder, Transformer encoder, multiple cross-domain fusion modules and the feature enhancement module corresponding to cross-domain fusion module one by one, and decoder.The application has the advantages that the soft tissue contrast of CT image data is low, it is difficult to distinguish cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue problem, by CNN encoder extraction local feature and Transformer encoder extraction global semantic feature, and local feature and global semantic feature dynamic interaction and fusion, accurately segmented advantage is carried out to whole brain cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue.

The application belongs to the field of brain white matter fiber track prediction, and relates to a brain white matter fiber track prediction method and system based on a pre-trained basic model. The method adopts an MAE architecture, and performs self-supervised pre-training on large-scale brain fiber track data through a mask-reconstruction mechanism to learn global topology and local geometric features of neural fibers. In the training stage, a hybrid progressive mask strategy is introduced. In the initial stage of the model, continuous segment masks are mainly used, and gradually transition to random point masks, so as to realize hierarchical learning of structural patterns and robust expression of features. After pre-training is completed, the model is fine-tuned, so that only the input of the two end point coordinates of the fiber can reconstruct and predict the intermediate track, and the fiber path inference based on the end point is realized. In the fine-tuning stage, the encoder structure is kept stable, the decoder is guided by the end point features to generate complete tracks conforming to the structural distribution of the brain white matter fiber, and the technical problems existing in the prior art are solved.

The invention provides a cerebral cortex thickness measurement method and system based on a level set image segmentation algorithm, and belongs to the technical field of neural image analysis, and the method comprises the steps: obtaining a brain magnetic resonance imaging image, and carrying out the skull stripping processing; performing three-dimensional visualization on the stripped image through volume rendering, and acquiring three-dimensional coordinates of points of interest and a region of interest on the surface of the cortex by using a preset interaction means; using a level set method to carry out brain grey matter and brain white matter segmentation on the region of interest to obtain a brain grey matter segmentation layer and a brain white matter segmentation layer, and storing three-dimensional coordinate data of points in the segmentation layers; calculating the minimum Euclidean distance according to the three-dimensional coordinate data of the points in the segmentation layer and the three-dimensional coordinates of the points of interest to obtain the cortex thickness of the points of interest; the method solves the problems that the existing cerebral cortex thickness measurement lacks a visual means, is not supported by a mathematical model, cannot be accurate to the thickness of an interest point, and cannot display and retain comprehensive information.

The invention provides a method for quantifying a water content ratio, which comprises the following steps of: scanning a brain by using first energy and second energy through dual-energy CT (Computed Tomography) equipment to generate plain scanning data of each voxel; setting the three substances of the dual-energy tomography three-substance separation mathematical model as water, grey matter and white matter; processing the plain scanning data of each voxel by using the dual-energy tomography three-substance separation mathematical model to obtain enhanced data of each voxel; and calculating the water content percentage of each voxel according to the enhancement data of each voxel. The method for quantifying the water content can quantify the water content of the encephaledema tissue of the acute stroke patient, and further quantify the edema proportion of the encephaledema tissue. The invention further provides a water content ratio quantifying device, a tomography system and a storage medium which relate to the method.

A method for evaluating mild cognitive impairment based on key fiber bundles. Since AD ​​patients are already in the middle and late stages when symptoms appear, and existing treatment methods are difficult to achieve effective results and can only delay the progression of the disease, the evaluation of mild cognitive impairment (MCI is in the intermediate stage between health and AD) is of great significance. The present invention selects diffusion tensor imaging magnetic resonance data as the main research object. This modality is a special form of magnetic resonance imaging and is currently the only non-invasive means to effectively observe and track brain white matter fiber bundles. It reflects multiple diffusion properties in brain white matter tissue. At the same time, unlike the previous pixel-level feature extraction of the entire magnetic resonance image, the present invention innovatively adopts fiber bundle-level features, that is, extracting key fiber bundles with significant differences between the mild cognitive impairment patient group and the healthy control group for feature fusion, which is a supplementary means to the traditional mild cognitive impairment evaluation method and can assist in the evaluation of mild cognitive impairment based on existing technologies.

This invention provides a parameter conversion method and system for multimodal brain network maps. The method includes: acquiring and comparing multiple source brain regions and multiple target brain regions of a single subject; constructing a set of overlapping brain regions between each target brain region and the source brain region; and statistically analyzing the number of white matter fiber tracts and brain functional connectivity coefficients between each set of overlapping brain regions. Based on this, the remapping coefficients of the white matter fiber brain network and the brain functional connectivity coefficient brain network are calculated respectively. The method also includes: acquiring and statistically analyzing the set of overlapping brain regions between the target brain regions and the source brain regions of multiple subjects, as well as the corresponding number of white matter fiber tracts and brain functional connectivity coefficients; calculating the variance of the brain connectivity strength of the first experimental group and the first control group under the source map, and the second experimental group and the second control group under the target map; and weighting and summing the source brain connectivity statistics between the overlapping brain region sets using influence weights to obtain the target brain connectivity statistics between the target brain regions.

The invention discloses an infant white matter injury cognitive prognosis evaluation method, system and device, and relates to the technical field of medical treatment, the method comprises the following steps: obtaining T1 weighted imaging and diffusion tensor images of the brain of a to-be-evaluated infant; delineating a focal white matter damage area on the T1 weighted image, and generating a binarized individual focus mask; preprocessing the diffusion tensor image to generate a partial anisotropic graph; based on the individual focus mask and the diffusion tensor image, performing parallel calculation on focus volume and white matter structure loss-of-connection scores involving fiber bundles; inputting the lesion volume involving the fiber bundle and the white matter structure loss of connection score into a pre-trained decision tree model, and outputting an assessment result of the infant cognitive development delay risk according to a preset judgment rule; according to the method, a new quantitative index is provided for PWML dominant lesions and can be derived to conventional MRI images for analysis, DTI data does not need to be collected, and a new solution is provided for early evaluation of prognosis.

The invention discloses a whole brain segmentation method, system and device and a medium, and the method comprises the steps: obtaining CT image data, and carrying out the preprocessing to obtain standard image data; and inputting the standard image data into the whole-brain segmentation model to obtain a whole-brain segmentation result. Wherein the whole-brain segmentation model is obtained based on training of sample labeling data, and the whole-brain segmentation model comprises a CNN encoder, a Transform encoder, a plurality of cross-domain fusion modules, feature enhancement modules in one-to-one correspondence with the cross-domain fusion modules, and a decoder. Aiming at the problems that CT image data is low in soft tissue contrast, and the brain grey matter, the brain white matter, the cerebrospinal fluid and the brainstem tissue are difficult to distinguish, local features are extracted through a CNN encoder, global semantic features are extracted through a Transform encoder, and the local features and the global semantic features are dynamically interacted and fused, so that the accuracy of the CT image data is improved. And the method has the advantage of accurately segmenting the whole brain grey matter, the white matter, the cerebrospinal fluid and the brainstem brain tissue.

The application discloses an ADHD nerve regulation method and system based on accurate target positions, and comprises the following steps: acquiring magnetic resonance image data of multiple patients, performing image preprocessing on the magnetic resonance image data to obtain MRI images, constructing a high-resolution brain white matter function graph according to the MRI images and a preset brain white matter graph, identifying abnormal brain function areas of the patients according to the high-resolution brain white matter function graph, performing feature extraction on the abnormal brain function areas to obtain target point information, inputting the target point information into a trained brain network to obtain brain function detection results, inputting the brain function detection results into an ADHD nerve regulation device to output target nerve regulation results, and reasonably utilizing multi-modal nuclear magnetic resonance images, extracting nodes with strong representation ability and discrimination as stimulation target points, and realizing non-invasive and accurate stimulation on the abnormal brain function areas, so that the working reliability and operation convenience of the ADHD nerve regulation are improved.

The invention discloses an infant dyspepsia early warning method based on cerebral white matter network propagation dynamics abnormity, and relates to the technical field of brain image analysis and intelligent risk prediction. According to the method, firstly, standard preprocessing is carried out on infant brain MRI or DWI image data, and a structural connection network is constructed based on a newborn template; then, a linear threshold model (LTM) is introduced to simulate the diffusion process of information in the brain network, and multiple propagation dynamic characteristics including propagation time, diffusivity, cooperative speed-up ratio, competitive indexes and the like are extracted; based on the above characteristics, a random forest regression model is used to predict cognition, language and movement development scales of 18 months old, and a key propagation brain region is identified through characteristic importance analysis. According to the method, non-invasive, automatic and structure-driven early development risk assessment can be realized, and the method has relatively high generalizability and clinical application potential.

The application discloses an infant brain T2 weighted magnetic resonance imaging optimization method based on a fast spin echo sequence. First, T1, T2 and PD quantitative imaging of the infant brain from 0 to 24 months old is collected to obtain T1, T2 and PD values of the infant brain white matter and gray matter regions, and according to the relationship characteristics of the infant brain white matter T2 value and the gray matter T2 value, the infant is divided into different month groups. Then, based on the 3D T2 weighted imaging of the variable flip angle fast spin echo sequence, the signal intensity of the infant brain white matter and gray matter under different refocusing flip angle chains is calculated through an extended phase graph algorithm, and the best flip angle chain design scheme of each group is determined with the maximum white matter / gray matter contrast as the target. The application fills the blank of the infant brain T2 weighted imaging optimization, formulates the best flip angle chain optimization scheme of different month groups, and thus significantly improves the contrast of the infant brain T2 weighted imaging.

A method for evaluating key fiber bundles in early mild cognitive impairment. The abnormal diffusion information of brain white matter caused by diseases such as Alzheimer's disease, which is currently the most studied, is a prominent manifestation of brain plasticity. Its impact on fibers includes changes in brain positioning and corresponding indicators. Fiber tracking can extract diffusion information in fibers. In addition to providing fiber direction, it can also display the diffusion activity of water molecules in brain cells and the network structure of the brain, providing relevant references for researchers to find brain changes caused by diseases. The present invention optimizes and improves the more mature fiber automatic quantification technology currently available, and at the same time adjusts the parameters of the Mahalanobis distance formula to clear and screen the fiber bundles initially obtained, which can more comprehensively and accurately extract the whole-brain fiber bundles. The verification of key fiber bundles with significant differences between the early mild cognitive impairment patient group and the healthy control group can provide certain reference value and help for the assessment and prediction of early cognitive impairment.

The invention relates to the technical field of image data processing or generation, in particular to a white matter lesion area intelligent positioning method based on an MRI image, and the method comprises the steps: constructing a white matter network based on a brain MRI image; clustering nodes in the white matter network to obtain a plurality of clusters; adjusting the attribution cluster of each edge node based on the difference and the attribution degree of each edge node and the corresponding adjacent clustering cluster; determining a final adjustment result under the condition that the adjustment result meets a preset condition; and determining the white matter gathering point corresponding to the adjusted node as the white matter lesion area. The method can improve the accuracy of determining the white matter lesion area.

The embodiment of the invention relates to a processing method and device of a lymphatic-like system function evaluation model. The method comprises the following steps: selecting a candidate region set in an I CBM-DT I-81 white matter map; constructing a first data set based on the ICBM-DT I-81 atlas, the candidate region set and the lymphatic-like system function evaluation results and diffusion tensor images of all research objects; constructing a lymphatic-like system function evaluation model, and training the model based on the first data set; after training is finished, according to a registration result of an ICBM-DT I-81 atlas and a brain diffusion tensor image of a testee, marking regions of interest of the candidate region set on the image of the testee, and according to all the regions of interest, carrying out left and right brain ALPS index calculation and generating an index feature tensor; and inputting the index feature tensor into a lymphatic-like system function evaluation model for prediction to obtain a prediction vector. According to the invention, the evaluation accuracy and the evaluation flexibility can be improved.

The present invention relates to a computer implemented method for determining a new prognostic index of a brain tumour, in particular glioblastoma. The new proposed index in particular is based upon the determination of alterations of the connections of the brain white matter measured with magnetic resonance diffusion techniques.

This invention discloses a method, apparatus, device, storage medium, and program product for generating a head model. The key features include receiving and verifying original 3D T1-weighted image data and an original 3D mesh model; performing tissue segmentation based on the verified 3D T1-weighted image data and the original 3D T1-weighted image data to obtain gray matter masks, white matter masks, scalp masks, and skull masks; performing topological repair on the gray matter and white matter masks to obtain a brain mask; converting the scalp mask, skull mask, and brain mask into scalp mesh models, skull mesh models, and brain mesh models, respectively; calculating and applying the spatial transformation matrix from the verified 3D mesh model to the scalp mesh model; and performing spatial registration and fusion of the scalp mesh model, skull mesh model, and brain mesh model to obtain the head model. The advantages are that it effectively improves the efficiency, accuracy, and robustness of head model generation and has good cross-platform deployment capabilities.

The invention provides a white matter fiber multi-dimensional geometrical morphology quantification method and system based on magnetic resonance imaging, and the method comprises the steps: reconstructing diffusion magnetic resonance imaging data of a whole-brain white matter fiber bundle, generating a three-dimensional fiber track, setting the sampling spacing of the fiber track according to a resolution ratio requirement, and carrying out the reconstruction of the diffusion magnetic resonance imaging data of the whole-brain white matter fiber bundle; sampling is carried out through a sub-voxel-level fiber trajectory interpolation algorithm; carrying out hierarchical iterative integral calculation point by point along a fiber track by adopting a sliding window through a path signature method, and generating a local path signature feature in each window; the path signature features of the whole-brain white matter fibers are emitted to the original magnetic resonance image, and a three-dimensional geometric feature map meeting the resolution requirement is generated. According to the method, through a multi-order tensor analysis and super-resolution track feature mapping technology of a path signature (PS), the problems that the spatial resolution of traditional diffusion indexes (such as an anisotropic fraction (FA) and an average diffusivity) is insufficient and a high-order geometric quantization tool is lacked are solved.

Provided are a method and device for automated brain white matter fiber tract segmentation combined with anatomical priors. The method includes: obtaining whole-brain fiber point coordinates and structural T1-weighted magnetic resonance images, determining superficial white matter fibers and deep white matter fibers based on the whole-brain fiber point coordinates, and generating an anatomical brain region division map based on the structural T1-weighted magnetic resonance images; determining an individual-level anatomical feature descriptor of each fiber based on the superficial white matter fibers, the deep white matter fibers and the anatomical brain region division map, and respectively determining a cluster-level anatomical feature descriptor corresponding to each fiber; and inputting the whole-brain fiber point coordinates, the individual-level anatomical feature descriptors and the cluster-level anatomical feature descriptors into a trained fiber tract segmentation model, and obtaining classification results of fiber tracts.

The present invention discloses a brain age prediction method and device based on regional segmentation. The method obtains a diffusion tensor image to be processed corresponding to the target measurement subject, determines multiple diffusion index data corresponding to each voxel in the diffusion tensor image to be processed, and generates multiple diffusion index images based on the multiple diffusion index data. The multiple diffusion index images are processed according to a brain fiber bundle template to obtain a first eigenvector corresponding to each brain region. The eigenvector is used to describe the image features corresponding to each brain region. The first eigenvector corresponding to each brain region is processed using a pre-trained brain age prediction model to obtain a brain age prediction result corresponding to the target measurement subject. The brain age prediction model is used to integrate the image feature information of each brain region in multiple diffusion index images to accurately obtain the white matter information of each brain region, and the brain age is accurately predicted based on the white matter information of each brain region, thereby improving the accuracy of the brain age prediction result.

A method of imaging a biological material having a component that exhibits an ultra-short transverse relaxation time after excitement by electromagnetic energy is disclosed. More specifically, balanced steady state free precession ultra-short echo time (bSSFP UTE) magnetic resonance imaging (MRI) is used in combination with 3D center-out trajectory data collection and image subtraction techniques, to yield accurate high spatial resolution images of a material component having an ultra-short transverse relaxation time. In an embodiment, the 3D center-out trajectory can be a 3D rosette k-space trajectory. The disclosed methods can be used to image, among other things, biological materials such as without limitation, cortical and trabecular bones, lung parenchyma, tendons, and ligaments. In a particular example, the disclosed methods are used to image the myelin bilayer in brain white matter, such as for example, to detect, treat, or monitor brain lesions in multiple sclerosis patients.

The invention provides a schizophrenia auxiliary diagnosis and abnormity positioning method and system, and relates to the technical field of artificial intelligence. According to the method, BOLD signal extraction is carried out on each grey matter brain region and white matter brain region in the preprocessed data set, and white matter bold signals are introduced into classification feature construction of schizophrenia for the first time, so that the dimension of brain function analysis is expanded; on this basis, a new TW3C feature is constructed to quantify collaborative activities between grey matter areas under the same white matter pathway, resonance / common collaborative information in the transmission process of grey matter and white matter neural signals is effectively extracted, brain area features with the most discriminative ability are extracted, and diagnosis and research of schizophrenia are facilitated. In addition, based on the classification feature set, an SVM classifier is used for classification, and the brain region corresponding to the feature with the high weight value is positioned.

The application discloses a kind of infant brain white matter injury cognitive prognosis evaluation method, system and device, it is related to medical technical field, the method includes obtaining the T1 weighted imaging and diffusion tensor image of the brain of infant to be evaluated;The local white matter injury area is outlined on T1 weighted image, and binary individual lesion mask is generated;The diffusion tensor image is preprocessed, and partial anisotropy map is generated;Based on individual lesion mask and diffusion tensor image, the lesion volume involving fiber bundle and white matter structure disconnection score are calculated in parallel;The lesion volume involving fiber bundle and white matter structure disconnection score are input into pre-trained decision tree model, according to the preset determination rule, the evaluation result of the risk of infant cognitive development delay is output;The method provides new quantitative index for PWML dominant lesion, can be derived to conventional MRI image and analyzed, without acquiring DTI data, and provides a new solution for early evaluation of prognosis.

This invention provides a brain tissue equivalent material for medical imaging equipment and its preparation method, belonging to the technical field of tissue equivalent materials. The brain tissue equivalent material of this invention comprises the following raw materials in the indicated mass fractions: 20-35% bisphenol A epoxy resin, 10-15% octyl glycidyl ether, 1-5% urea, 1-5% calcium carbonate, 1-5% sodium dihydrogen phosphate, 1-5% dipotassium hydrogen phosphate, 15-35% polyethylene, 15-25% methyl methacrylate, and 5-15% trimethylhexanediamine. The CT value of the brain tissue equivalent material of this invention is highly consistent with that of real brain tissue, successfully covering the typical range of gray and white matter. The brain tissue equivalent material exhibits excellent wide-spectrum attenuation characteristics; within the commonly used energy range of 60-200 keV in clinical CT equipment, the deviation of the X-ray attenuation coefficient from the target brain tissue material is controlled within 5%, demonstrating excellent spectral stability and simulation accuracy.

This invention discloses a distance function-based method for coupling the structure-function connectivity of white matter fiber tracts, comprising: acquiring brain imaging data and preprocessing it; extracting structural attributes and functional signals of white matter fiber tracts from the preprocessed brain imaging data; constructing a structural connectivity matrix and an individualized functional connectivity matrix based on the structural attributes and functional signals of white matter fiber tracts; measuring the similarity of connection weights between corresponding columns of the structural connectivity matrix and the functional connectivity matrix using a distance function; and coupling the structure-function connectivity of white matter fiber tracts based on the similarity of connection weights. This method can quantify the relationship between the structure and function of brain white matter fiber tracts and is applicable to research on brain structure and function analysis, brain development, and mental illnesses based on white matter fiber tracts.

The invention belongs to the technical field of biological pharmacy, and provides application of glycyrrhizin in preparation of a medicine for treating white matter injury. Animal experiment results show that the HMGB1 inhibitor Gly can obviously improve rat WMI pathological changes, reduce rat brain white matter region HMGB1 expression, obviously reduce the number of rat brain white matter region NG2 + Olig2 + double positive cells and improve rat WMI afterbrain white matter region OLs differentiation disorder; the myelination disorder of the white matter region of the rat after WMI can be improved, and the number of myelination axons of the rat is obviously increased; and the learning ability and the spatial memory ability of the rats are improved. It is clear that Gly can inhibit expression of HMGB1, and an effective technical means is provided for treatment of white matter damage.

The invention provides a parameter conversion method and system for a multi-modal brain network atlas, and the method comprises the steps: obtaining and comparing a plurality of source brain regions and a plurality of target brain regions of a single subject, constructing an overlapping brain region set of each target brain region and the source brain region, counting the number of white matter fiber bundles and a brain function connection coefficient between the overlapped brain region sets, and respectively calculating remapping coefficients of a white matter fiber brain network and a brain function connection coefficient brain network on the basis of the number and the brain function connection coefficient; an overlapped brain region set of the target brain regions and the source brain regions of the multiple subjects and the corresponding white matter fiber bundle number and brain function connection coefficients are obtained and counted; and calculating the variance of the brain connection strength of the first experimental group and the first control group under the source map and the variance of the brain connection strength of the second experimental group and the second control group under the target map, and performing weighted summation on the source brain connection statistical magnitude between the overlapped brain region sets through the influence weight to obtain the target brain connection statistical magnitude between the target brain regions.

The present invention discloses a method for segmenting superficial cerebral veins in an image, which belongs to the technical field of medical image processing and comprises the following steps: S1: selecting several healthy subjects; S2: performing conventional sequence scanning and susceptibility-weighted imaging; S3: SWI raw data processing; S4: constructing a data set; S5: data set preprocessing; S6: model training; S7: model application; S8: model extension application; the present invention combines the development advantages of susceptibility-weighted imaging, constructs a deep learning model based on MinIP images, and realizes preliminary quantitative analysis of the segmentation of morphological features of superficial cerebral veins, which can provide an objective reference method for the evaluation of superficial cerebral veins in clinical neurological diseases in the future. Based on this model, the hypothesis proposed by tissue anatomy that pathological changes of superficial cerebral veins can cause microinfarctions and thus lead to high signals in the white matter of the brain is discovered from an imaging perspective, and the gender difference in the distribution of the number of blood vessels in the superficial cerebral veins is discovered.