48 results about "Grey matter" patented technology

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system, method, medium and device based on the non-invasive multi-modal neural image.According to the Alzheimer's disease auxiliary prediction system and method based on the non-invasive multi-modal neural image.Through a multi-modal brain network fusion graph neural network framework guided by graph reconstruction, a self-supervised normal form is used for fully utilizing MRI data, the limitation of rare PET data under a supervision strategy is broken through, and and reliable pathological characterization embedding is generated. Meanwhile, different from conventional brain network modeling which only constructs node features, the method constructs a bimodal brain network which integrates semantic and topological information according to gray matter function signals and white matter fiber structure connection, and designs a node-edge bidirectional encoder, so that the representation capability of the brain network is remarkably improved.

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 invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

There is provided a method of reconstruction of a target brain structure(s), comprising: reconstructing at least a part of a brain comprising boundaries of brain structures that include the target brain structure(s), wherein the reconstruction is insufficient for parceling of the target brain structure(s) into sub-structures of a same type of gray or white matter, reconstructing and parceling the target brain structure(s) using a reference atlas, segmenting and parceling at least one originating brain structure using the reference atlas, filtering white matter fibers to isolate at least one target white matter tract connecting the originating brain structure(s) and the target brain structure(s), and creating a 3D reconstruction of the target brain structure(s) and the target white matter tract(s), wherein the target white matter tract(s) and the target brain structure(s) are transformed and / or mapped to an anatomical native space.

The invention provides a schizophrenia core epicenter region identification method based on a multi-mode nerve image, and belongs to the technical field of medical image analysis and neuropsychiatric disease diagnosis. According to the method, a macroscopic-mesoscopic-microscopic covariant network model is established by integrating multi-modal nerve image data (including structural magnetic resonance, functional magnetic resonance imaging and diffusion tensor imaging) and mesoscopic-microscopic scale data (gene expression atlas, neurotransmitter distribution and cell construction characteristics). The method comprises the following steps: firstly, constructing various connection networks, then identifying a core epicenter region with abnormal grey matter thickness based on a graph theory algorithm, carrying out cross-scale matching on the epicenter region and gene expression, neurotransmitter and cell structure characteristics by utilizing spatial correlation analysis, and analyzing a diffusion path of the epicenter region through connection omics. By fusing the multi-scale biomarkers, the limitation of single modal analysis is broken through, and systematic analysis of the schizophrenia pathological network is realized.

The invention discloses a grey matter brain region anti-magnetic component susceptibility map construction and analysis method and application thereof, which can be used for exploring a global distribution mode of cortical iron deposition of an AD patient on a voxel level and positioning a cortical region with susceptibility related to cognition. The method comprises the following steps: (1) carrying out MR data acquisition on a subject on a magnetic resonance scanner; (2) unwrapping the multi-echo phase image by using a phase unwrapping method based on Laplacian; performing brain stripping processing on the amplitude image of the first echo by using a BET algorithm built in FSL software and generating a binary brain mask image; removing a background field of the unwound phase diagram by using a V-SHARP algorithm in combination with the brain mask diagram; calculating a magnetic susceptibility map from the local field map by using an STAR-QSM algorithm; (3) calculating the cortex thickness by using the T1 weighted structure image, and performing post-processing on the 3D-FSPGR sequence structure image; and (4) carrying out voxel-based whole-brain QSM analysis and cortex thickness statistical analysis.

The invention provides a mental disease classification method and system based on an individual difference structure covariant network and machine learning, and belongs to the field of mental disease classification. The problem of classification performance bottleneck caused by heterogeneity of mental diseases in the prior art is solved. The method comprises the following steps: acquiring a structural magnetic resonance T1 weighted image, and preprocessing the image; performing brain region segmentation on the pre-processed T1 image based on an AAL brain map, and extracting the gray matter volume of each brain region; constructing an IDSCN network by calculating the Pearson's correlation coefficient of the brain grey matter volume of the paired brain regions; calculating the area under a node topological attribute curve of the IDSCN network; screening node attribute indexes with statistical differences between the patient group and the healthy control group through double-sample t test; and taking the screened node attribute indexes as feature vectors, and inputting the feature vectors into a support vector machine classification model for disease classification. The method is mainly used in the medical image processing field.

The invention discloses a cerebral cortex penetrator small blood vessel magnetic resonance imaging method based on an inflow enhancement effect. The method comprises the following steps: modeling a penetrator small blood vessel to generate a blood vessel TOF (Time of Flight) image; calculating the blood vessel-grey matter contrast ratio of the blood vessel TOF image under different FAs and different TRs, and optimizing the FAs and the TRs according to the blood vessel-tissue contrast ratio; obtaining a structural image of the in-vivo brain tissue, performing cortex reconstruction, and calculating an angle of a surface normal of each cortex vertex relative to the B0 field to obtain a cortex angle graph; adjusting a single-layer blood vessel scanning plane angle according to the cortex angle diagram; and according to the optimized FA and TR and the single-layer blood vessel scanning plane angle determined in the step S4, carrying out magnetic resonance imaging on the cerebral cortex perforation small blood vessel. The invention further discloses a cerebral cortex puncturing small blood vessel magnetic resonance imaging device based on the inflow enhancement effect. According to the method and the device, magnetic resonance imaging of the cerebral cortex perforation small blood vessel can be realized under the non-invasive and contrast-agent-free conditions.

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 discloses application of TESC as an Alzheimer's disease prevention and treatment target and relates to the field of biological medicines. A TESC overexpression (TESC-OE) hippocampal neuron cell line is constructed through lentivirus transfection, and the result shows that TESC-OE has the ability to resist A beta-induced neuron apoptosis and synapse damage. A TESC overexpression model in the hippocampus of a mouse is constructed through stereotactic injection of an adeno-associated virus, and the result shows that the hippocampal gray matter volume in the brain of the TESC-OE mouse is significantly larger than that of a wild type mouse. A beta stereotactic modeling is performed on the basis of TESC overexpression in the hippocampus, and the result shows that the TESC-OE mouse can significantly resist A beta-induced hippocampal atrophy, learning and memory dysfunction and impaired synaptic plasticity. Immunoblotting analysis reveals the molecular mechanism that TESC can increase the expression of anti-apoptotic genes in the hippocampus and reduce the expression of pro-apoptotic genes to exert a neuroprotective effect.

The application relates to the technical field of image processing, in particular to an MRI image feature extraction method for dynamic evaluation of child brain development, which comprises the following steps: determining a cerebrospinal fluid coefficient of a child brain; dividing an MRI image into a cerebrospinal fluid region and a residual brain region based on the cerebrospinal fluid coefficient of the child brain of each pixel point; determining a white matter coefficient of each pixel point in the residual brain region according to the gray value of each pixel point in the decayed weighted image of each pixel point in different gradient directions, and the gray value of each pixel point in the first weighted image and the second weighted image in the residual brain region; segmenting the white matter region and the gray matter region of the residual brain region based on the white matter coefficient, and extracting image features in the white matter region and the gray matter region. In this way, the accuracy and reliability of the segmentation result of different tissue regions in the child brain MRI image are improved.

A method is to be implemented by a computing device that stores a risk assessment model, and includes steps of: obtaining an entry of target physiological data and target magnetic resonance imaging (MRI) images of a brain of a subject with mild cognition impairment (MCI); obtaining, based on the target MRI images, voxel values respectively of primitive voxels that are related to grey matter of the brain of the subject; selecting, from among the primitive voxels, any primitive voxel satisfying a filtering criterion as a selected voxel; calculating an average of the voxel value(s) respectively of the selected voxel(s) to obtain an average target voxel value; and obtaining a probability of the subject developing Alzheimer's disease within a predetermined time period by feeding the average target voxel value and the entry of target physiological data into the risk assessment model.

The invention relates to the technical field of virtual reality, in particular to a virtual reality-combined neurosurgery preoperative simulation training method, which comprises the following steps of: acquiring a grey matter boundary and analyzing a nerve fiber trend, delimiting a path to form a training path, screening out a path range in contact with a functional area, loading track comparison and extracting an overlapping path, and performing simulation training on the overlapping path. Identifying the corresponding relation between the path abnormity and the area, and recombining the action sequence and the execution range according to the problem distribution. According to the invention, by identifying the spatial continuity of nodes in an image, combining the contact condition of a path and a functional region, screening a response region which can be included, comparing the spatial correspondence between an operation track and the nodes, mining offset and fracture points in the path, and determining the spatial continuity of the nodes. The action sequence is rearranged according to the frequent occurrence positions of the actions in the path and the continuation relation of the nodes, so that the training path has continuity and fluency, the action connection between the regions is optimized, and the coordination and consistency of the path and the operation content are improved.

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.

The invention belongs to the technical field of medical image analysis, and particularly discloses a method and system for predicting the electroshock treatment effect of a teenager patient with severe depression, and the method comprises the steps: collecting the structural magnetic resonance imaging data of the teenager patient with depressive disorder before and after electroshock treatment ECT, and carrying out the preprocessing; obtaining brain grey matter morphological characteristics of the teenager depressive disorder patient; the method comprises the following steps: constructing a brain structure connection network of a patient based on brain grey matter morphological characteristics of a teenager depressive disorder patient; based on a brain structure connection network and a graph theory, extracting topological features of a brain network; and inputting the extracted brain network topology features into a two-stage machine learning model for curative effect prediction. According to the technical scheme, the most representative key information is extracted, and the brain structure network features and the machine learning technology can be effectively fused, so that the prediction accuracy of the ECT curative effect of the teenager MDD patient is improved.

The disclosed system provides an integrated neuroimaging analytics platform for comprehensive preoperative neurosurgical planning, intraoperative neurosurgical guidance and post-operative assessment by processing multimodal MRI (structural, diffusion, functional, and angiography) and CT data. The platform performs detailed anatomical characterization by delineating tumor subregions (peritumoral edema, enhancing tumor, and necrotic core), segmenting brain tissues (gray matter, white matter, and CSF), and executing lobe, cortical / subcortical parcellation. Advanced 3D rendering visualizes tumors alongside critical white matter fibre tracts derived from diffusion MRI, while MRA data is used to segment cerebrovascular structures, and functional MRI analysis identifies eloquent cortices associated with motor, speech, and visual functions. All results are integrated within a user-friendly GUI featuring advanced multiplanar slicing and a smart brush for interactive mask editing, complemented by a speech-to-text engine for streamlined analytical reporting. This comprehensive approach facilitates precise and efficient surgical planning, thereby enhancing patient safety and improving clinical outcomes.

The application discloses a two-stage radiomics lesion identification and positioning method and device, adopts a multilayer perceptron network to analyze multi-modal image data, detects FCD by extracting features taking the gray matter as a region of interest by using a radiomics method, and the features combine shape, first-order statistics and texture features from multi-modal and wavelet images. In addition, the application also introduces asymmetric features of left and right hemispheres, avoids potential interference in the contralateral area of the unilateral FCD patient caused by the compensatory mechanism of the left and right hemispheres of epilepsy. According to the rich high-dimensional features and asymmetric features of radiomics, the sensitive features of FCD are fully explored, the two-stage detection method is combined to identify FCD abnormalities, the extracted features are more perfect, false positive results are avoided, and the accuracy and sensitivity of detecting FCD are improved from different scales from coarse to fine granularity.

The application provides a brain cortex thickness morphological analysis method and system of MR images, and relates to the technical field of medical image data processing.The method comprises the following steps: obtaining a head three-dimensional high-resolution magnetic resonance T1 weighted image sequence of a patient with intractable epilepsy to be evaluated; performing bias field correction and brain tissue stripping on the image sequence to construct an initial brain parenchyma three-dimensional voxel model containing individual lateral ventricle expansion morphological characteristics; based on the initial brain parenchyma three-dimensional voxel model, extracting an initial isosurface of the interface between gray matter and white matter; calculating a local curvature tensor for a deep brain sulcus region in the initial isosurface; performing topological defect repair and mesh smoothing on the initial isosurface according to the local curvature tensor to obtain an individualized cortex surface mesh model eliminating ventricle expansion deformation artifacts.The application realizes accurate registration of image space and surgical physical space.

The present application relates to neuromodulation and brain-computer interface technical field, propose a kind of robust optimization method and system under the condition of parameter uncertainty, robust optimization method includes constructing five-layer head tissue model including skin, skull, cerebrospinal fluid, grey matter and white matter based on the magnetic resonance image of patient, and the independent conductivity uncertainty radius of each layer of tissue is set, multi-dimensional ellipsoid uncertainty set is constructed;Min-Max worst case cost function is constructed and is solved, guarantee in ellipsoid set under the worst case, electrode current weight meets safety constraint;Worst case cost function is converted into second-order cone programming form using dual principle;Pseudo-trace perception sliding window mechanism is introduced to monitor the continuity of physiological signal or impedance data, when detecting non-physiological mutation, trigger re-optimization by calling historical stable parameters and amplifying uncertainty radius;When re-optimization has no solution, trigger safety derating mechanism, and the current instruction of previous period is scaled proportionally.

The invention discloses an oxygen uptake cardiopulmonary endurance testing method based on load turn-back walking and a human brain structure, and relates to the technical field of exercise physiology and biomedicine detection.The method comprises the steps that progressive load turn-back walking exercise data and a high-resolution brain structure image of a subject are obtained; extracting key brain region parameters such as the thickness of the anterior cinerary cortex, the grey matter density of island leaves and the signal intensity of brainstem respiratory center; normalizing the motion features and brain structure parameters and then constructing a 12-dimensional multi-modal fusion feature vector; and inputting a deep feedforward neural network model to predict the maximum oxygen uptake, and performing five-level cardiopulmonary endurance evaluation according to an age and gender correction result. According to the method, quantitative association between peripheral exercise performance and a central nervous anatomy basis can be realized, the heart and lung endurance evaluation precision of old people and individuals with abnormal neurological functions is remarkably improved, and rapid screening and personalized exercise prescription making are supported.

A biomechanically realistic brain model for impact testing comprises white matter simulant materials and gray matter simulant materials positioned to correspond with anatomical brain structure. The white matter simulant comprises anisotropic hydrogels with embedded magnetically-responsive, electrically-responsive, thermally-responsive, and / or mechanically-responsive particles that exhibit directionally-dependent stress-strain responses. The gray matter simulant comprises isotropic hydrogels or silicones, particularly siloxanes, that exhibit uniform stress-strain responses to applied forces. The materials are cast, injected, printed, or formed in anatomically correct positions and share realistic interfaces. The brain model accurately imitates physical brain responses during impact tests, particularly angular impacts, providing realistic testing results for biomechanical analysis.

A biomechanically realistic brain model for impact testing comprises white matter simulant materials and gray matter simulant materials positioned to correspond with anatomical brain structure. The white matter simulant comprises anisotropic hydrogels with embedded magnetically-responsive, electrically-responsive, thermally-responsive, and / or mechanically-responsive particles that exhibit directionally-dependent stress-strain responses. The gray matter simulant comprises isotropic hydrogels or silicones, particularly siloxanes, that exhibit uniform stress-strain responses to applied forces. The materials are cast, injected, printed, or formed in anatomically correct positions and share realistic interfaces. The brain model accurately imitates physical brain responses during impact tests, particularly angular impacts, providing realistic testing results for biomechanical analysis.

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.

The present invention discloses a zero-shot MRI brain tumor image generation method, belonging to the field of image generation. The method comprises the following steps: S1: acquiring and preprocessing data without a brain tumor sample; S2: constructing a brain tumor shape generation method; S3: constructing a brain tumor texture generation method; S4: constructing a method for simulating tumor mass effect and capsule effect; and S5: generating zero-shot MRI brain tumor images based on the aforementioned brain tumor shape generation method, texture generation method, and mass effect and capsule effect simulation methods. First, a lesion-free brain tumor dataset undergoes brain extraction and white matter segmentation. In step S2, a mathematical model is established to simulate tumor shape, generating core tumor regions, edema regions, and complete tumor regions. In step S3, texture simulation is performed using Gaussian noise and cubic spline interpolation, and Gaussian filtering is used to avoid oversharpening of the generated texture. Finally, in step S4, local scaling and warping are used to simulate tumor mass effect and capsule effect, respectively. This method generates zero-shot MRI brain tumor images using traditional image processing algorithms rather than deep neural networks, eliminating the need for tumor samples. The method increases the controllability of image generation, allowing precise adjustment of specific features in the image, such as tumor size, shape, and location.

The invention discloses a Parkinson's disease DBS postoperative curative effect prediction system based on group horizontal structure brain network similarity, and belongs to the technical field of biomedical image mode recognition. According to the system, on the basis of brain structure magnetic resonance image data of healthy subjects and Parkinson's disease patients before operation, grey matter volumes in 16 networks defined by AAL are used as feature vectors, Spearman rank correlation among the vectors is calculated, and similar matrixes among the subjects are constructed in two groups respectively. Secondly, after the matrix is averaged, the matrix is input into a binary logistic regression classification framework as features, and a classification model related to the DBS operation curative effect is established; according to the system, heterogeneity between subjects is quantified by constructing a group level structure brain network similarity matrix, classification of Parkinson's disease patients and normal people and intelligent prediction of DBS operation curative effects are achieved by using the heterogeneity as characteristics, and more comprehensive brain image information reference can be provided for preoperative accurate evaluation.

The application discloses a brain region target point division method for autism neural regulation, which comprises the following steps: acquiring resting-state functional magnetic resonance imaging data of an autism patient, and pre-processing the functional magnetic resonance imaging data; extracting back-lateral prefrontal cortex ba9 and ba46 in the pre-processed data as target brain regions by using a Brodmann template; unfolding the pre-processed image data into a two-dimensional time sequence, and performing sliding window processing on the time sequence to obtain a plurality of sliding window time sequences; calculating a functional connection matrix of voxel points in the target brain region and all voxel points in the whole brain gray matter in each sliding window time sequence; performing dimension reduction processing on all the functional connection matrices based on local similarity to obtain a reduced dimension functional connection matrix; acquiring a reference clustering template of a normal person which has been constructed, performing Kmeans clustering on the functional connection matrix of the autism patient according to the clustering cluster number, and obtaining sub-region division of the target brain region.

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.