108 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 an attention deficit hyperactivity disorder subtype recognition method based on brain network topology hub deviation, relates to the technical field of medical diagnosis, and has the technical key points that a norm model of a brain structure form similarity network is constructed based on multi-center big data; quantifying a brain network topology hub index as a target phenotype of the norm model; then, performing semi-supervised clustering analysis by utilizing the individual deviation phenotype of the topological hub index so as to divide different biological subtypes and reveal unique clinical and biological characteristics of the biological subtypes; finally, strict cross validation is carried out in an external independent queue, and it is ensured that the potential biological subtypes recognized by the typing model have good generalization and clinical effectiveness.

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.

According to the neural regulation target positioning method and system based on magnetic resonance and electroencephalogram fusion and the focus brain tissue head model construction method based on deep learning, the individual brain structure can be accurately simulated, and a reliable basis is provided for subsequent analysis; according to the electroencephalogram source imaging method based on deep learning, a brain source can be effectively positioned from complex electroencephalogram signals; a target network accurate positioning method based on source imaging further locks and regulates a target spot and a target brain network. The combination of the three can give full play to the advantages of multi-modal data, achieves more accurate and efficient target spot and target network positioning by means of the powerful ability of deep learning, and provides powerful support for nerve disease diagnosis and treatment.

The invention relates to the technical field of medical data processing and nerve regulation and control, in particular to a method for synchronously activating post-stroke neuroplasticity through photoacoustic, which comprises the following steps of: 1, constructing a stroke specific brain region target map: fusing brain structure connection data and functional network data of a patient, generating a three-dimensional target map containing the high-metabolism semi-dark band coordinate set and the cross-hemisphere compensatory connection intersection; 2, calculating a time-space synchronization focusing parameter; 3, synchronous stimulation and dynamic regulation are executed, wherein the ultrasonic transducer and the laser source are controlled to emit time-space synchronous sound waves and light pulses; monitoring the blood perfusion variable quantity and the neurotransmitter concentration ratio of the target brain area in real time; when the blood perfusion variable quantity does not reach the expectation or the transmitter ratio is unbalanced, the stimulation intensity is dynamically adjusted, and the focusing coordinate is translated in the direction away from the infarction area. Through photoacoustic stimulation of time-space synchronization, the difference between the propagation speeds of sound waves and light waves is overcome, and accurate collaboration of neural restoration is achieved.

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 utility model belongs to the technical field of medical instruments, in particular to a wearable transcranial electrical stimulation device, which comprises a transverse hoop and a vertical hoop, the transverse hoop is fixedly connected with a mounting block, the vertical hoop is adjusted with the transverse hoop by sliding in the mounting block, one end of the transverse hoop and one end of the vertical hoop are respectively provided with a clamping groove, and the transverse hoop and the vertical hoop are connected with the mounting block. The transverse hoop and the vertical hoop adjust the sliding clamping rod through the clamping grooves, the transverse hoop connecting clamping rod is provided with a sliding way, the interior of the sliding way is connected with a transverse electrode plate in a sliding mode, meanwhile, the bottom of the transverse hoop connecting clamping rod is rotationally connected with a connecting piece, the connecting piece is rotationally connected with a support, and the support is rotationally connected with an auricle cover. The size of the transverse hoop and the vertical hoop of the device can be adjusted, the clamping rod can slide and rotate, the position of the electrode plate can be accurately adjusted, the device can adapt to the head circumference, the head shape and the brain structure difference of different users, it is ensured that the electrodes make good contact with the scalp, and accurate and effective electrical stimulation is achieved.

The invention discloses a DTI-based brain network analysis method for a refractory OAB patient, and relates to a method for applying diffusion tensor imaging (DTI) and graph theory analysis to explore a central nervous regulation mechanism of the refractory OAB patient. 43 cases of refractory OAB patients and 46 cases of matched healthy contrasts are selected for DTI scanning. The method comprises the following steps: evaluating an overactive bladder symptom score table (OABSS), an overactive bladder symptom questionnaire table (OAB-Q), a Hamilton anxiety scale (HAM-A) and a Hamilton depression scale (HAMD) of all subjects, and recording related clinical data. A DTI and graph theory analysis method is adopted to explore the change of global and local topological attributes of the brain structure network of the intractable OAB patient. And further performing brain network function connection analysis on the discovered differential brain region as a seed point.

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.

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.

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 provides a brain network construction and analysis method based on multivariable analysis. The method is mainly used for multi-view analysis of a brain structure and a functional network. The technical problem to be solved is that information loss is caused by neglecting necessary multivariate relationships among brain region nodes when univariate analysis is carried out on a brain function network and a brain structure network. According to the method, the canonical correlation analysis method based on data driving is used for constructing the single-mode brain network, the problem that a traditional univariate analysis method neglects necessary multivariate relations is avoided, the complex relation between the brain structure and functions can be reflected more accurately, and a more reliable basis is provided for deep research of a brain working mechanism.

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 present invention provides a method for identifying mental illness based on pulse structure-function brain network coupling, which belongs to the field of intelligent auxiliary medical diagnosis technology and effectively solves the technical problem of the neurobiological mechanism between structural connection and functional connection that is often ignored in the traditional diagnosis of mental illness. Its technical solution is: first, functional and structural brain networks are extracted from functional magnetic resonance imaging and diffusion tensor imaging; then, the information feature maps of these two brain networks are extracted through BrainNetCNN; then a pulse coupled neural network is constructed to learn the brain structure-function coupling mechanism, so as to derive the pulse structure-function coupling; finally, the obtained coupling information is input into the classification layer to obtain the disease identification result, and the cross-entropy loss function is used to train and optimize the result. The beneficial effects of the present invention are: the present invention helps to deeply understand the neural mechanism of mental illness and has a wide range of application prospects in clinical applications.

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 invention provides a method, system, medium, and device for constructing a brain network based on mechanical parameters, comprising: step S1: acquiring a brain structural image and a mechanical parameter distribution image using magnetic resonance elastography, registering the brain structural image and the mechanical parameter distribution image, segmenting them using a standard spatial atlas, and extracting the mechanical modulus value of each pixel in each brain region; step S2: calculating the probability distribution function of each brain region and estimating the similarity of the probability distribution functions of each brain region using KL divergence; and step S3: using the calculated KL divergence values ​​as connections between different brain regions, constructing a mechanical network and performing graph theory analysis, comparing graph theory indicators between healthy individuals and patients with neurodegenerative diseases. The present invention can improve the efficiency of early diagnosis of neurodegenerative diseases.

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 present invention provides a fetal brain tissue segmentation method, device, medium and terminal based on a cycle consistency network. By acquiring target fetal brain image data, inputting the target fetal brain image into a pre-trained cycle consistency network, and outputting a segmentation result of the target fetal brain image corresponding to the gestational week; The present invention adopts a learning framework combining cycle consistency and adversarial learning, and realizes the accurate capture of fine-grained domain-invariant brain structures in thick clinical and thin reconstruction cases of fetal brain MR images.

The present invention discloses a method, system, device and medium for extracting brain features from MRI images, and relates to the technical field of intelligent biomedical signal processing. When extracting imaging brain features related to the function of the cerebral lymphatic system and the brain structure and morphology in T1WI sequence images, T2WI sequence images and DTI sequence images, the present invention fully exploits the deep features of multiple MRI sequence images. The extracted imaging features related to the function of the cerebral lymphatic system and the brain structure and morphology include the diffusion degree values ​​of the bilateral brain tissue in the patient's brain when converted into fiber bundles, and the fiber bundle reference values ​​that cause the fiber bundles to deviate when the patient's brain discharges. The fiber bundle reference values ​​can reflect which parameters are diffusion parameters when the bilateral brain tissue is abnormal. The diffusion degree values ​​and diffusion parameters reflected by these imaging features are closely related to the patient's epileptic language disorder function and can be used as standards for predicting language disorders in epileptic patients.

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 construction method and device for a brain structure connection atlas, and the method comprises the steps: obtaining a three-dimensional fluorescence microscopic image of brain cells; calculating a response function according to the gradient distribution of the unidirectional fiber groups; according to the gradient weighted sampling data and the response function of each voxel v in the three-dimensional fluorescence microscopic image, constructing a brain structure connection map; the gradient weighted sampling data at each voxel v represents discrete sampling in each direction of the gradient spherical continuous distribution function within a predetermined range near each voxel v. According to the method, the three-dimensional orientation distribution of the white matter nerve fibers of the brain can be obtained through analysis, then the three-dimensional brain structure connection diagram is constructed according to the fiber orientation distribution, and the method has the advantages of being high in speed, high in precision and capable of achieving three-dimensional analysis.

The present invention relates to a device and method for monitoring the cerebral blood oxygen saturation. The device comprises a power supply and modulation module, a light source module, a light guide module, an optical detection module, and a data processing and analysis module; under the action of the power supply and modulation module, light emitted by the light source module is transmitted into an area to be monitored within the brain of a test subject by means of a transmission optical fiber of the light guide module; after the light optically interacts with the brain tissue in said area within the brain of the test subject, a portion of the light enters the transmission optical fiber; the transmitted light is received by the optical detection module and processed by the data processing and analysis module to calculate and obtain continuous blood oxygen saturation data. The device and method for monitoring the cerebral blood oxygen saturation of the present invention adopt a spectroscopy method with a relatively direct measurement principle, are designed and developed using an optical fiber recording technology with a deep measurement depth and a minor impact on the activities of the test subject, and thus can be used for real-time blood oxygen saturation monitoring in an animal brain including the deep brain structure.

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.