70 results about "Cerebral structure" patented technology

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses a mental disorder intelligent discrimination system based on a brain structure image similarity map, and belongs to the technical field of artificial intelligence medical image analysis. The system firstly collects brain structure magnetic resonance image data of a multi-center mental disorder patient and a healthy control and carries out standardization preprocessing; then constructing a brain structure standardized distribution model of the cross-age gender, and extracting individualized deviation degree features; establishing a typical brain structure characteristic spectrum database of various mental disorders through a neural network; after to-be-diagnosed individual features are vectorized, multi-dimensional parallel comparison calculation is carried out on the to-be-diagnosed individual features and the atlas database by using a special similarity quantization algorithm; and the system outputs a quantitative similarity score vector containing the matching degree with various mental disorders and health modes, and generates a structured differential diagnosis report. According to the invention, diagnosis normal form transformation from absolute classification to flexible matching is realized, the problem of identification of mental disorder heterogeneity and common diseases is effectively solved, and the interpretability and clinical credibility of an intelligent diagnosis system are remarkably improved.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The invention relates to the technical field of medical information processing, in particular to a multi-modal data integrated analysis system for screening children with autism, which comprises a data acquisition module, a data processing module, a feature extraction module, a feature fusion module, a data screening module, a data analysis module and a data output module, according to the system, behavior data, brain function data and brain structure data of autism children are collected, and after preprocessing and feature extraction are carried out, fusion processing is carried out by a feature fusion module. The feature fusion module comprises a multi-scale topological feature representation sub-module, a heterogeneous feature integration sub-module and a self-adaptive weight adjustment sub-module which are respectively used for realizing multi-scale representation, heterogeneous feature integration and dynamic weight adjustment of features; the feature fusion technology solves the problems of feature scale inconsistency, inter-modal semantic gap, feature importance dynamic change and the like in multi-modal data fusion, improves the autism screening accuracy and early recognition capability, and supports personalized screening and accurate intervention.

The invention provides an Alzheimer's disease intervention rehabilitation system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of medical health. The system comprises a data acquisition unit, a correction unit, a stimulation unit and an evaluation feedback unit. The data acquisition unit is used for acquiring multi-dimensional data such as electroencephalogram signals and pathology associated data of a user and extracting target neural oscillation features from the multi-dimensional data; a correction unit constructs a brain structure-pathology association model, and performs pathology association correction on the target neural oscillation features; and the stimulation unit generates co-stimulation containing at least two modes of light, sound, magnetism and vibration based on the corrected features, configures stimulation parameters according to a predetermined time domain or frequency domain relationship, and intervenes the coupling target. And the evaluation feedback unit evaluates the intervention effect by collecting the stimulated multi-dimensional data. According to the method, the multi-mode stimulation is bound with the pathological-neural oscillation coupling characteristics, so that accurate and collaborative intervention aiming at the pathophysiological mechanism of the Alzheimer's disease is realized.

Disclosed herein are methods and devices for transvascular placement of electrodes on a surface of a brain or in deep brain structures for the purpose of neuromodulation. The device can comprise a delivery catheter comprising a lumen. The device can comprise a piercing assembly extending through the delivery catheter, wherein the piercing assembly comprises a piercing assembly catheter and a needle. The device can comprise one or more electrodes configured to contact the brain tissue; and wherein the piercing assembly catheter comprises an opening in a wall thereof such that when the delivery catheter and the piercing assembly catheter are positioned within a vessel, the needle extends through the opening to puncture a vessel wall.

The invention relates to the technical field of psychiatric disease diagnosis, and discloses a defective schizophrenia prediction and analysis system based on multi-modal brain network characteristics, which comprises an image data acquisition module, an image data processing module, a deep learning model module and a prediction module which are connected in sequence, the image data acquisition module acquires a brain MRI image; the image data processing module carries out cortical and subcortical reconstruction on the brain MRI image to obtain a model input index; the deep learning model module performs training by using a training data set composed of a plurality of brain MRI images and defective schizophrenia diagnosis results thereof to obtain a target prediction model; and the prediction module inputs the real-time brain MRI image into the target prediction model to obtain a prediction result. According to the method, the multi-dimensional brain structure image features are integrated, diagnosis information contained in brain region changes is fully mined, prediction results of defective schizophrenia and non-defective schizophrenia are improved, and a more comprehensive biological basis is provided for clinical diagnosis.

The invention provides a depression detection method, system and equipment based on a multi-modal brain image and a medium. The method comprises the following steps: acquiring a functional magnetic resonance image and a structural magnetic resonance image of the brain of a subject; performing collaborative analysis on the time sequence change information of the functional magnetic resonance image and the spatial relationship of the brain region, and extracting brain function characteristics representing brain function activity characteristics from the functional magnetic resonance image; carrying out collaborative analysis on voxel distribution and regional hierarchical relationship in the structural magnetic resonance image, and extracting brain structure features representing brain tissue morphology from the structural magnetic resonance image; inputting the brain function features and the brain structure features into a cross-modal interaction module, and performing cross-modal feature fusion on the brain function features and the brain structure features to generate cross-modal brain features; and inputting the cross-modal brain features into a classification module to obtain a depression detection result of the subject. According to the invention, the depression identification precision can be greatly improved.

The application discloses a kind of based on brain structure image's hepatic encephalopathy evaluation method and system, the method includes: obtaining the brain structure image data of subject brain;Based on brain structure image data, the feature parameter of characterizing brain tissue morphology is extracted;The characteristic parameter is handled by pre-training disease progression evaluation model, and the space-time progression trajectory of brain structure abnormality is inferred;Based on space-time progression trajectory, the disease subtype and disease progression stage to which the subject belongs are determined;Based on disease subtype and disease progression stage, evaluation information for characterizing the disease state of hepatic encephalopathy is generated.The space-time progression trajectory of brain structure abnormality is inferred based on cross-sectional image data, which overcomes the dependence on massive longitudinal tracking data, realizes the objective typing and staging of hepatic encephalopathy based on neurobiology, thereby effectively solving the technical problems of difficult to track disease evolution and individualized evaluation.

The invention discloses a dynamic trajectory construction and visual mapping method for association of mental disorder brain network damage and systemic system diseases, and belongs to the field of artificial intelligence medical application. According to the method, high-resolution MRI images, biomarkers and clinical information of major mental disorder patients are collected, and the influence of factors such as age, gender, medication and diagnosis on the braingut axis and the cardio-cerebral axis is evaluated through multi-modal data fusion. By constructing a disease dynamic trajectory model, brain structures and function change modes corresponding to different mental disorders are identified. Large-scale samples are analyzed through machine learning, potential risks and protection factors are extracted, and a visual tool is developed to visually display changes of the brain under different disease systems. A closed-loop feedback mechanism is established through follow-up visit, the disease progress and the intervention effect are dynamically tracked, and key evaluation indexes are identified. According to the invention, theoretical basis and practical guidance are provided for early screening, precise intervention and personalized treatment of mental disorders, and the diagnosis and treatment accuracy and efficiency are remarkably improved.

The application discloses a brain structure network weight construction method based on microstructure quantitative characteristics. The method comprises the following steps: firstly, a brain structure fiber bundle sampling point template is obtained by processing a MNI space brain fiber bundle template; then, a population data set is acquired by processing the brain structure fiber bundle sampling point template according to an improved multi-modal magnetic resonance imaging method; an attention variational autoencoder model is constructed; the population data set is input into the attention variational autoencoder model for training; finally, the vector value of the brain structure feature map of a to-be-tested individual is input into the trained attention variational autoencoder model for processing, and the processing result is directly used as the weight value of an edge in the brain structure network. The application overcomes the information imbalance caused by the fiber bundle length difference, provides multiple different microstructure information of a multi-modal brain, and realizes providing a new direction for the brain structure network weight research field.

Examples of the presently disclosed technology provide new systems and methods for real-time temperature propagation and tissue damage visualization during laser interstitial thermal therapy (LITT) procedures that do not rely on real-time MR imaging. Accordingly, examples enable performance of LITT procedures in regular operating rooms lacking MR-equipment-thereby reducing costs and improving availability for LITT procedures. Examples achieve these advantages by leveraging “discretized” patient-specific 3D brain structure representations to perform numerical methods for solving partial differential equations that estimate real-time (or close to real-time) temperature propagation within a patient's brain during a LITT procedure.

This application provides a disease intelligent diagnostic device based on brain structural connectivity identifiers and its application. The device includes (1) a data acquisition module; (2) a data processing module: reconstructing the spin distribution function of the data and distributing the spin distribution function in a standard space; (3) a data projection module: projecting the spin distribution functions of patients and healthy individuals into the standard space to obtain the Z-value of the maximum direction within the voxel; and (4) a Connectome Identifier extraction module: reducing the dimensionality of the Z-value to form a 1-dimensional feature value. The device of this application can be used for the diagnosis of various brain diseases and the determination of lesion location and severity.

The present application relates to the technical field of disease classification model training, in particular to a brain disease classification model training method, device, equipment and storage medium. The brain function connection network and the brain structure connection network complement each other, and the combination of the two can provide more human physiological information. Therefore, the brain connection network formed by the fusion of the brain function connection network and the brain structure connection network has more human physiological information. The brain disease classification model is learned and trained by the brain connection network, so that the brain disease classification model can learn more human physiological information. Therefore, the brain disease classification model after training can provide a classification result with higher accuracy for brain diseases.

The application relates to a variational correlation evaluation method and device, which comprises the following steps: acquiring magnetic resonance image data to obtain to-be-processed data; determining an iteration position sequence of the to-be-processed data and extracting iteration features to obtain a training set and a test set; inputting the training set into a variational correlation evaluation classification model for iteration training and testing, judging whether the trained variational correlation evaluation classification model converges or not, and obtaining the variational correlation evaluation classification model when the model converges, and performing effective feature extraction based on the variational correlation evaluation classification model; determining each effective feature position sequence, converting the effective feature position sequence into a three-dimensional brain structure matrix, covering the three-dimensional brain structure matrix to a preset standard human brain template, and identifying effective features related to each stimulation condition. The application quantifies the contribution of a single voxel in the process of executing a specific cognitive function, and finally identifies and extracts the least amount of features that can best represent the target stimulation condition.

The invention discloses a brain image VR system developed based on unity and blender. The system comprises a nerve fiber direction sensing coloring module, a multi-brain structure model intelligent splitting and memorizing module, a double-data-set layered precise control module, an intelligent profile generation module, a double-end adaptive intelligent slicing module and a customized VR immersive interaction module. The invention further provides a brain image VR method based on unity and blender development. According to the method, the display and rendering efficiency problem of the brain image OBJ model in Unity is effectively solved; the comfort of model observation in the VR scene is improved; and the adaptation stability of the system to different brain image data is ensured.

The invention provides a foramen ovale migraine target-oriented electroencephalogram signal analysis method and system, and relates to the technical field of signal processing.The method comprises the steps that spatial registration is conducted on electrode coordinates and brain structure images, and a mapping relation is established; obtaining an initial coordinate of an oval foramen migraine associated target brain region, screening a target associated electrode according to the mapping relation, and finely adjusting the initial coordinate; configuring electroencephalogram acquisition equipment parameters, and acquiring original electroencephalogram signals; and performing preprocessing and feature extraction, performing analysis processing on the target feature set through a pre-trained feature quantitative analysis model, and outputting a feature similarity score and a visual analysis report of the target electroencephalogram signal. The technical problems that in the prior art, due to the fact that the signal collection range is wide, noise interference of an irrelevant area is serious, effective signals of a target area are submerged, and the signal analysis precision is further affected are solved, accurate processing of the electroencephalogram signals of the foramen ovale migraine associated target is achieved, and the signal analysis precision is improved.

The application discloses a brain structure analysis method and system based on gray matter and white matter imageomics hierarchical coupling, and the method comprises the following steps: acquiring and preprocessing original three-dimensional T1 weighted structure magnetic resonance images, generating gray matter volume smoothing images and white matter volume smoothing images; extracting brain region level imageomics original features from the gray matter volume smoothing images and the white matter volume smoothing images respectively, obtaining a gray matter brain region level original feature set and a white matter brain region level original feature set; based on repeated scanning data, performing stability evaluation screening, same brain region cross-gray matter and white matter tissue candidate screening and redundancy removal processing on the gray matter brain region level original features and the white matter brain region level original features, and generating a brain region level reserved feature list; in each brain region, performing feature layering on the brain region level reserved feature list, and generating a first-order statistical feature layer and a texture feature layer; and constructing a gray matter and white matter coupling index based on the first-order statistical feature layer and the texture feature layer, so as to form a brain region level gray matter and white matter hierarchical coupling matrix.

The application discloses a brain surgery path intelligent planning method based on reinforcement learning and relates to the technical field of brain surgery. A standardized surgery environment data set is formed; a three-dimensional brain structure topology model is obtained; a dynamic three-dimensional constraint tensor is generated; a surgery timing decision Token sequence is formed; the surgery timing decision Token sequence is input into a Decision Mamba path decision network based on a selective scanning state space model to perform strategy optimization, and an underlying obstacle avoidance candidate path decision vector sequence is output; a safe candidate path sequence meeting an anatomical safety constraint is obtained; an executable surgery trajectory instruction set is generated; the dynamic three-dimensional constraint tensor and the surgery timing decision Token sequence are updated and recalled, and path decision and safety correction are re-executed. The application improves the response speed and precision of intraoperative path adaptive re-planning in a strong dynamic environment of brain tissue stress deformation.

The application provides an ultrasonic craniocerebral tomography method based on a physically embedded neural network, and steps are as follows: firstly, according to the distribution characteristics of a craniocerebral biological tissue, a real physical CT brain model is converted into a craniocerebral acoustic velocity distribution map; secondly, according to the craniocerebral velocity distribution map and the physical characteristics of craniocerebral ultrasonic signal propagation, a sound field time domain signal is acquired; thirdly, the sound field time domain signal is subjected to data preprocessing to acquire a frequency domain data matrix, and a numerical simulation database is established; then, a neural network structure based on a full waveform inversion algorithm and embedding physics is built, and the network is trained by using the craniocerebral acoustic velocity distribution map and the frequency domain data matrix to obtain a combination of optimal network hyperparameters; finally, the frequency domain data matrix to be predicted is input into the network to realize high-precision and real-time ultrasonic craniocerebral tomography. The application realizes quantitative imaging and evaluation of brain structure, and has the advantages of fast imaging speed and high imaging precision.

Provided herein are targeting peptides and vectors containing a sequence that encodes the targeting peptides that deliver agents to specific substructures in the brain. Specifically, the targeting peptide is a component of a modified, sequence-specified adeno-associated virus (AAV) capsid protein further wherein the brain substructure may be the globus pallidus, putamen, internal capsule, caudate, claustrum, substantial nigra, motor cortex, insula,.temporal cortex, thalamus, hippocampus, subiculum, and deep cerebellar nuclei.

Systems and methods for neuronavigation in accordance with embodiments of the invention are illustrated. Targeting systems and methods as described herein can generate personalized stimulation targets for the treatment of mental conditions. In many embodiments, direct stimulation of a personalized the stimulation target indirectly impacts a brain structure that is more difficult to reach via the stimulation modality. In various embodiments, the mental condition is major depressive disorder. In a number of embodiments, the mental condition is suicidal ideation.

Disclosed herein are methods and devices for transvascular placement of electrodes on a surface of a brain or in deep brain structures for the purpose of neuromodulation. The device can comprise a delivery catheter comprising a lumen. The device can comprise a piercing assembly extending through the delivery catheter, wherein the piercing assembly comprises a piercing assembly catheter and a needle. The device can comprise one or more electrodes configured to contact the brain tissue; and wherein the piercing assembly catheter comprises an opening in a wall thereof such that when the delivery catheter and the piercing assembly catheter are positioned within a vessel, the needle extends through the opening to puncture a vessel wall.

The application relates to the technical field of medical health early warning, in particular to an intracranial pressure monitoring and early warning system based on imaging features, which comprises the following modules: a brain structure twin construction module, which is used for acquiring multi-modal image data and constructing brain structure twins; a brain imaging feature extraction module, which is used for obtaining brain imaging features; an intracranial pressure inversion atlas generation module, which is used for acquiring the distribution response relationship of the brain imaging features and generating an intracranial pressure inversion atlas; an intracranial blood flow regulation capacity quantification module, which is used for acquiring transcranial Doppler ultrasound data and quantifying an intracranial blood flow regulation capacity index; an intracranial pressure state evolution module, which is used for obtaining an intracranial pressure state evolution curve; and an intracranial pressure monitoring and early warning module, which is used for generating graded early warning thresholds and triggering monitoring and early warning. The application realizes dynamic quantitative early warning suitable for individual physiological characteristics by acquiring the evolution trend of intracranial pressure.

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.

The invention relates to an electroencephalogram traceability model training method and device, equipment and a medium. The method comprises the following steps: constructing a standardized graph structure data set containing multiple individual electroencephalogram signals, a structure connection group and a source activity true value, and training a graph neural network by adopting a meta-learning framework to extract a common rule of a cross-individual brain connection group and a traceability mapping relationship, so as to form a pre-training model with strong generalization ability; for a new individual, only key parameters associated with connection group features in the model are adjusted through a parameter efficient fine tuning technology, and rapid migration of pre-training meta-knowledge to individual specific connection is realized; finally, while individualized traceability precision is kept, computing resources and data volume required for model adaptation are greatly reduced, the problem of traceability deviation caused by individual brain structure difference in a traditional method is effectively solved, and feasibility and efficiency of an electroencephalogram traceability technology in clinical practice are remarkably improved.

The invention discloses a method and a system for classifying Alzheimer's disease. The method comprises the following steps of: firstly, respectively preprocessing original data of three-dimensional structural magnetic resonance imaging (sMRI) and four-dimensional resting state functional magnetic resonance imaging (rs-fMRI); then, brain structure features are extracted through a three-dimensional visual Transform model fusing the dynamic combinable multi-head attention mechanism and a multi-layer feature fusion module, and space-time function features are extracted based on a SwiFT model; secondly, performing intra-modal sparse screening and inter-modal bidirectional interaction on the two features through a bidirectional sparse cross attention mechanism to obtain a fusion feature; and finally, outputting a classification probability result of the Alzheimer's disease patient, the mild cognitive impairment person or the healthy person based on the fusion features. According to the method, the accuracy and reliability of classification are improved through an advanced deep learning model and an efficient multi-modal feature fusion strategy.

This invention provides a method and system for improving the segmentation accuracy of smaller categories of brain structures in whole-brain structure segmentation. The method involves inputting acquired 3D brain images into a trained FCN brain classification network and a trained MAD brain classification network for segmentation, respectively. The segmentation results from the two networks are then fused to improve the segmentation accuracy of smaller categories of brain structures in whole-brain structure segmentation based on the FCN brain classification network. This invention considers the balance between whole-brain data input segmentation and GPU memory usage during FCN network training for whole-brain segmentation. FCN networks typically perform convolutional downsampling on the input images, leading to information loss and affecting the recognition of smaller categories of brain structures. Therefore, this invention proposes a method for reclassifying smaller categories of brain structures. Finally, the results are merged with the original FCN network results to achieve high-precision segmentation of each brain structure.

The application relates to the technical field of brain network construction, and particularly discloses a brain structure and metabolic image synchronously acquired brain network construction method and device, which comprises the following steps: acquiring a structure image and a metabolic image to be processed; registering the structure image to be processed to a standard brain template; registering the metabolic image to the standard brain template by taking the structure image as an intermediate; fusing brain structure information and metabolic information of the nuclear medicine image to obtain a metabolic image; performing brain partition processing on the metabolic image to form a multi-partition brain model; obtaining a co-correlation matrix by analyzing the correlation between multiple brain regions; and obtaining a metabolic brain network by optimizing the co-correlation matrix of the brain model. The brain structure and metabolic image synchronously acquired brain network construction method and device can more effectively find metabolic differences between brain disease patients and normal people in network attribute indexes, and provide valuable information for brain development, maturity and aging.

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.