40 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 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.

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 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 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.

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.

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.

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 invention discloses a brain MRI (Magnetic Resonance Imaging) structure segmentation method and system and electronic equipment, and the method comprises the steps: carrying out the preprocessing of a three-dimensional brain structure image, and dividing the preprocessed data into a training set, a test set and a verification set; inputting the training set and the verification set into a brain structure segmentation network model constructed based on Transform and a convolutional neural network to train a deep learning model; evaluating the segmentation effect of the model through a test set, and calculating an error value between a brain structure prediction result and a real result by using a mixed loss function; and performing multiple iterations and parameter updating optimization on the brain structure segmentation network model according to the error value. The inherent local information extraction capability of the convolutional layer makes the global information extraction capability of the network insufficient, while the Transform global feature information extraction module establishes a global feature extraction advantage through a self-attention mechanism and further refines the features by using the convolutional layer, and the two are combined to realize advantage complementation.

Systems and methods are provided for targeting neuromodulation. A first image, representing a structure of the brain, is acquired from a first imaging system and a second image, representing a connectivity of the brain, is acquired from either the first imaging system or a second imaging system. A first utility value associated with directly modulating tissue within a region of interest is determined for each of a plurality of voxels within the region of interest from the first image. A second utility value associated with indirectly modulating tissue outside of the region of interest by modulating tissue within the region of interest is determined for each of the plurality of voxels from the second image. An overall utility value for each of the plurality of voxels is determined from the first utility value and the second utility value, and an optimal location is determined from the overall utility values.

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 invention discloses a method, apparatus, electronic device, and storage medium for modeling forward EEG problems. The method includes: segmenting brain tissue structures based on brain structural images; dividing the head domain into a global mesh based on the segmented brain tissue structures; creating a skin-like electrode template based on the electrode line distribution of skin-like electrodes; locally refining the mesh based on the center position, shape, and electrode line cross-sectional diameter of the skin-like electrodes to obtain a head model; obtaining a skin-like electrode model on the head model with electrode locations based on the skin-like electrode template; obtaining a corresponding source model based on mesh division of the cerebral cortex surface; and constructing a personalized EEG forward EEG model using the finite element method based on the head model, source model, and skin-like electrode model. This invention addresses the problem of complex electric field distribution in the scalp region covered by the electrodes due to the complex fractal serpentine mesh structure of skin-like electrodes, thus improving the modeling accuracy of forward EEG problems.

This invention discloses an on-orbit intelligent processing method for spaceborne images based on brain-like computing, belonging to the field of on-orbit intelligent processing of spaceborne images. This invention utilizes a spiking neural network to simulate the structure of the human brain: including a feature extraction layer, a pulse coding layer, an STDP learning layer, a lateral inhibition layer, and a decision output layer; and improves model accuracy through on-orbit updates and federated learning. This invention reduces computational energy consumption and improves efficiency. Simultaneously, this invention leverages the synaptic plasticity of neurons to construct a SNN with excellent spatial and temporal resolution.

A system for electrical stimulation and recovery of impressed currents during sleep to measure the electrical impedance of intracranial tissue through a single-frequency current stimulation, and decrease the brain impedance through other stimulation parameters, thereby increasing extracellular space and improving glymphatic flow (as indexed dynamically by the concurrent brain impedance measure). A skin-path-correction factor is estimated to allow the subtraction of the electrode-to-skin impedance and thereby estimate the brain impedance compartment separately. Based on computational modeling of electrical conductivity of head tissues, the electrodes are placed at forehead and nuchal sites to optimize current flow through high-conductive skull orifices. Current flow estimation is monitored at the critical orifice of the foramen magnum, and safety limits are monitored and enforced for individual electrodes and for key brain structures.

The present disclosure discloses a model construction method and device, a storage medium and an electronic device, which are used for constructing a brain-like model of a pulse neural network based on biological brain topology constraints, and relate to the technical field of computational neuroscience. The present disclosure solves the problem of lack of biological rationality of the brain-like model of the pulse neural network. The model construction method comprises: dividing brain regions of to-be-processed functional magnetic resonance imaging data to obtain M brain region image data; generating M model nodes based on the M brain region image data; generating N model edges based on a correlation coefficient matrix between the M model nodes; screening the N model edges based on a preset network topology threshold to obtain S model edges meeting a preset condition; generating a topology constraint of a brain-like model based on a biological brain function network based on the M model nodes and the S model edges; and constructing the brain-like model based on the topology constraint. The present disclosure can improve the biological rationality of the brain-like model constructed based on the pulse neural network.

The application relates to the technical field of medical big data processing and artificial intelligence auxiliary diagnosis, in particular to an early Alzheimer's disease diagnosis method, which comprises the following steps: preprocessing and registering structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) image data of a to-be-diagnosed object, constructing a mixed feature pyramid to extract multi-scale anatomical features, adopting a space-time manifold embedding module to extract dynamic functional features, strengthening pathological correlation features through a cross-dimension double attention mechanism, and finally realizing diagnosis classification through multi-modal feature adaptive fusion. The application can accurately capture the deep correlation between brain structure microlesions and functional network abnormalities, and significantly improve the diagnosis accuracy of Alzheimer's disease and early mild cognitive impairment.

The application belongs to the technical field of medical information and neuroscientific technology, and particularly relates to an early screening method and system for Alzheimer's disease based on eye movement tracking, comprising the following steps: collecting human eye movement data of a subject during the execution of a cognitive task; constructing a personalized eye movement dynamics model based on the human eye movement data, extracting an eye movement behavior feature vector; utilizing a neural-eye movement isomorphic mapping relationship; identifying a neurodegenerative attenuation feature related to Alzheimer's disease; and detecting whether an epilepsy-like discharge feature exists in the virtual neural function signal. Through the construction of the neural-eye movement isomorphic mapping relationship, the virtual neural function signal of the deep brain structure can be reconstructed by using the eye movement data, the screening cost is effectively reduced, the evaluation accuracy of the function state of the hippocampus and amygdala is improved, and through the comorbidity correction of the epilepsy-like discharge feature, the accuracy and reliability of the early screening of Alzheimer's disease are significantly improved.

The application discloses a kind of positive problem modeling method, device, electronic equipment and computer readable storage medium, wherein, method mainly includes: (1) the physical equation description of electroencephalogram positive problem based on electrode stereoscopic structure boundary condition;(2) with the positive problem finite element construction of electrode stereoscopic structure and head brain structure;(3) conductive matrix calculation.The application proposes the positive problem modeling method based on electrode stereoscopic structure description, while constructing the normal current distribution of electrode coverage area, further constructs the tangential current distribution caused by electrode inner tangential voltage difference, which on the one hand increases the description of tangential current, makes the electric field distribution in electrode more fine, on the one hand, because of the advantage of its stereoscopic structure, can more detailedly, more intuitively describe the distribution of conductivity in electrode, so as to further make the electric field description in electrode more fine.

The present application relates to the field of medical magnetic resonance image reconstruction and intraoperative navigation, and particularly relates to an intraoperative magnetic resonance image reconstruction method and system based on medical registration guidance, comprising: acquiring preoperative high-field magnetic resonance images and intraoperative low-field magnetic resonance images of the same patient; inputting the preoperative high-field magnetic resonance images and the intraoperative low-field magnetic resonance images into a trained image processing model; performing registration by taking the preoperative images as the moving images and the intraoperative images as the fixed images through a registration network, generating a deformation field from the preoperative to the intraoperative, and correcting the brain structure displacement caused by the surgery; performing spatial transformation on the preoperative images based on the deformation field to obtain high-field images aligned with the intraoperative images; and fusing the aligned high-field prior information and the intraoperative low-field image features by using a reconstruction network to reconstruct high-quality target images. The present application effectively improves the definition and detail resolution of the intraoperative images by introducing individualized preoperative prior, and provides more accurate image support for surgical navigation.