55 results about "Structural magnetic resonance imaging" patented technology

The invention relates to an ADHD classification diagnosis method based on a multi-modal attention fusion network. The ADHD classification diagnosis method comprises the steps of data preprocessing, feature extraction, cross-modal attention fusion, Mama module application, feature fusion and classification and loss function and model optimization. The present invention improves the diagnostic accuracy of ADHD by integrating functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and phenotypic data (including age, gender, average intelligence quotient, etc.

A computer system that performs quality control (QC) on images associated with diffusion and structural magnetic resonance imaging (MRI) is described. This computer may include: a computation device that executes program instructions; and memory that stores the program instructions. During operation, the computer system may automatically perform a set of validation operations, where, when one or more of the validation operations fails, the images are rejected. Moreover, the set of validation operations may include: performing QC on brain-tissue segmentation; performing QC on diffusion MRI processing; and performing QC on bundles determined from the images using a tractometry technique.

The invention relates to the technical field of determination of nerve regulation and control precise stimulation targets, and discloses an individualized multi-frequency regulation and control stimulation system for Rett regulation and control. The individualized multi-frequency regulation and control stimulation system comprises a data acquisition module, a structure and function brain image analysis module and a multi-frequency electroencephalogram self-adaptive regulation and control module, the data acquisition module is used for acquiring resting state functional magnetic resonance imaging data rs-fMRI, structural magnetic resonance imaging data sMRI and EEG data, and the structural and functional brain image analysis module is used for identifying an abnormal brain area of an RTT patient and determining an optimal stimulation target. The multi-frequency electroencephalogram self-adaptive regulation and control module comprises two sub-modules including an electroencephalogram state recognition module and an FUS self-adaptive stimulation module, and the electroencephalogram state recognition module is used for analyzing EEG signals, extracting frequency band power change and instantaneous phase information corresponding to a target brain region and providing real-time feedback signals for self-adaptive regulation and control. According to the system, the target spot is accurately determined, and the individualized accurate stimulation target spot is determined by combining the structure and function image and the disease exposure model.

A method for performing multi-voxel spectroscopy includes obtaining structural magnetic resonance imaging data of a brain of a subject acquired with a magnetic resonance imaging scanner. The method also includes performing skull stripping on the structural magnetic resonance imaging data to generate a skull stripped brain image. The method further includes utilizing a trained deep learning-based segmentation model to generate a lesion core mask from the brain image. The method also includes locating a slice with largest volume of lesion present in the lesion core mask. The method includes calculating a voxel volume that avoids aliasing from the slice based on a field of view. The method includes automatically selecting a volume of interest in the brain having both the lesion and normal brain tissue for a multi-voxel spectroscopy scan by the magnetic resonance imaging scanner based on the brain image, the lesion core mask, and the voxel volume.

The present invention provides a method for extracting a neuroimaging biomarker based on an interpretable ensemble three-dimensional convolutional neural network (3DCNN) to address limitations in the prior art. The present invention derives a novel neuroimaging biomarker P-score from prediction results obtained by an ensemble three-dimensional convolutional neural network model. The solution can help researchers to conduct studies on longitudinal trajectory changes of structural magnetic resonance imaging (sMRI) during the progression of Alzheimer's disease, and analyze an association of the longitudinal trajectory changes with neurodegenerative changes of Alzheimer's disease subjects. The extracted neuroimaging biomarker can provide a basis for predicting a sequence of intervention of brain regions in the neurodegenerative changes of Alzheimer's disease patients and upcoming clinical symptoms.

The invention provides a multi-modal brain network classification method based on feature decoupling and dynamic graph construction, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining functional magnetic resonance imaging data and structural magnetic resonance imaging data of a to-be-detected person, and obtaining multi-modal brain network data according to the functional magnetic resonance imaging data and the structural magnetic resonance imaging data, and processing the multi-modal brain network data based on a preset multi-modal brain network classification model to obtain the brain network state of the to-be-tested person. According to the method, by combining functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data, the advantages of the two modes can be fully utilized, so that the accuracy and comprehensiveness of brain network state classification are improved, a dynamic graph attention module based on a graph attention network (GAT) can capture the dynamic change characteristics of a brain network, dynamic graph representation is constructed, and the classification accuracy of the brain network state is improved. And the sensitivity of the model to the dynamic connection relationship between brain regions is enhanced.

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

The invention discloses an image processing method, device and equipment based on multi-modal neural image fusion, and relates to the technical field of image recognition. The method comprises the following steps: calculating a functional connection matrix among regions in brain nerves based on functional magnetic resonance imaging data; extracting a first feature of the structural magnetic resonance imaging data and a second feature of the functional link matrix; obtaining a first feature vector and a second feature vector corresponding to the first feature and the second feature, and adding the first feature vector and the second feature vector to obtain a common feature vector; and performing feature enhancement on the first feature vector and the second feature vector through the common feature vector, splicing the enhanced first feature vector and the enhanced second feature vector to obtain a spliced feature vector, and inputting the spliced feature vector into a pre-constructed feature classification network to obtain a classification result. According to the scheme, complementarity among different modal data can be enhanced, so that diagnosis of depression is better assisted.

The invention provides a structural magnetic resonance imaging analysis method and system and a storage medium, and the method comprises the steps: obtaining sample data which is a T1 weighted structural image subjected to structural magnetic resonance imaging, determining a map of morphological indexes of a region of interest in an original brain network space according to the sample data, re-sampling the map to a preset standard brain network space template, carrying out Gaussian kernel smoothing processing to obtain standard brain network space template data, mapping the standard brain network space template data to a pre-constructed brain map template, determining feature input of a machine learning model according to the mapped brain map template, and carrying out the feature input of the machine learning model; and training the classification performance of the machine learning model based on the feature input to obtain a classification model. The effectiveness and reliability of training can be improved, so that the classification accuracy of the classification model obtained based on training can be improved.

The application provides a multi-modal brain network classification method based on feature decoupling and dynamic graph construction, and relates to the technical field of artificial intelligence, and the method comprises the following steps: acquiring functional magnetic resonance imaging (fMRI) data and structural magnetic resonance imaging (sMRI) data of a to-be-tested person, obtaining multi-modal brain network data according to the fMRI data and the sMRI data, processing the multi-modal brain network data based on a preset multi-modal brain network classification model, and obtaining the brain network state of the to-be-tested person. The application can fully utilize the advantages of the two modalities by combining the fMRI data and the sMRI data, thereby improving the accuracy and comprehensiveness of the brain network state classification, and the dynamic graph attention module based on a graph attention network (GAT) can capture the dynamic change characteristics of the brain network, construct a dynamic graph representation, and enhance the sensitivity of the model to the dynamic connection relationship between brain regions.

The invention discloses an Alzheimer's disease classification prediction method and system. According to the method, firstly, a bimodal brain network diagram is constructed, brain region features serve as nodes, and edge features are constructed through single nucleotide polymorphism data and structural magnetic resonance imaging data; the method is characterized in that closed-loop information interaction between nodes and edges is realized through a mutual feedback backflow graph neural network: firstly, edge features are dynamically updated based on node similarity, and information transmission from the nodes to the edges is realized; carrying out aggregation propagation and key screening among edge features by utilizing a topological neighborhood relationship; and finally, returning the edge feature information subjected to cross-modal fusion to the nodes, and updating node features. According to the method, the limitation that an existing graph neural network only pays attention to edge-to-node one-way information flow is overcome, and by describing dynamic coupling of nodes and edges and complementation of multi-modal information on a connection layer, the capturing capacity of early and tiny pathological modes of the Alzheimer's disease is enhanced, so that the accuracy and interpretability of classification prediction are improved.

The invention provides an image processing method and an image processing device, and relates to the field of computers. According to the method, feature extraction is carried out on multi-modal image data of functional magnetic resonance image data and structural magnetic resonance image data of a brain, feature fusion is carried out, classification is carried out based on fused features, an image recognition result is obtained, feature extraction is carried out based on a channel attention and space attention decoupling method, and the recognition accuracy is improved. According to the method, the image features can be extracted more accurately, the accuracy of the image recognition result is further improved, and diagnosis of mental diseases such as bidirectional affective disorder and Alzheimer's disease can be better assisted.

The invention provides a multi-modal MRI classification method and system, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a structural magnetic resonance imaging sMRI image and a functional magnetic resonance imaging fMRI image of a testee; inputting the two images into an attention guidance dynamic classification neural network, predicting a label of the testee, and obtaining a classification result; according to the attention guidance dynamic classification neural network, two groups of features are extracted from the sMRI image and the fMRI image, the two groups of features are fused by using a multi-scale attention sub-network, and classification is carried out based on the fused features. According to the invention, an attention guidance dynamic classification neural network is designed, different brain networks are constructed for testees, cross-modal inclusiveness integration and cross-modal consensus filtering operation are combined, feature extraction is realized in a shallow layer aspect, feature selection and feature classification are further carried out through a multi-scale attention sub-network, and the accuracy of feature classification is improved. Therefore, the detection and diagnosis precision of brain diseases can be improved.

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

The invention relates to a system and method for automatic volume of interest prescription for multi-voxel brain proton spectroscopy acquisition and post-processing. A method for performing multi-voxel spectroscopy includes obtaining structural magnetic resonance imaging data of a brain of a subject acquired with a magnetic resonance imaging scanner. The method also includes performing skull dissection on the structural magnetic resonance imaging data to generate a skull dissection brain image. The method further includes generating a lesion core mask from the brain image using a trained deep learning based segmentation model. The method also includes locating a slice having a maximum lesion volume present in the lesion core mask. The method includes calculating a voxel volume that avoids aliasing from the slice based on the field of view. The method includes automatically selecting a volume of interest in the brain having both the lesion and normal brain tissue for a multi-voxel spectral scan by the magnetic resonance imaging scanner based on the brain image, the lesion core mask, and the voxel volume.

The provided is a personalized target selection method for a non-invasive neuromodulation technology, including: preprocessing functional magnetic resonance imaging (fMRI) data from MRI scan data of a current patient to acquire fMRI brain image feature data; inputting the fMRI brain image feature data into a pre-trained inter-subtype classification model to acquire a subtype label of the current patient and all feature voxels of the subtype label; preprocessing T1-weighted MRI data of structural magnetic resonance imaging (sMRI) data from the MRI scan data of the current patient to acquire a skull outline and a transformation matrix between the sMRI and fMRI data; performing coordinate transformation on the feature voxels, calculating a distance between each voxel on the skull outline and each feature voxel, marking response feature voxels, and counting a number of response feature voxels; and sorting the number of response feature voxels.

This invention relates to the field of brain network analysis technology, specifically to a multimodal brain network fusion method based on tensor spectral clustering. The method includes: acquiring multimodal brain network data, including structural magnetic resonance imaging (SMRI) data, functional magnetic resonance imaging (fMRI) data, and diffusion tensor imaging (DTI) data; preprocessing the multimodal brain network data; constructing individual morphological brain networks, functional brain networks, and structural brain networks based on the preprocessed multimodal brain network data, and performing normalization processing; fusing the normalized multimodal brain networks using a tensor spectral clustering algorithm to obtain a fused brain network; acquiring the subject's behavioral data, inputting the behavioral data into the fused brain network, and calculating the subject's behavioral prediction values. This invention not only advances research in neuroscience, cognitive science, and psychology but also provides strong support for related clinical applications and personalized medicine.

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 discloses a graph convolutional neural network (GCN) disease prediction system based on multimodal brain MRI images, belonging to the field of medical image analysis and disease prediction technology. The system includes: an image preprocessing module that acquires and standardizes raw functional and structural magnetic resonance imaging (fMRI) data to generate a corresponding dataset; a dynamic supernet construction module that extracts spatiotemporal fusion variability features of brain regions; a multimodal fusion module that fuses features and constructs an adjacency matrix; a GCN prediction module that calculates prediction results such as disease progression grading; and a results visualization module that locates abnormal brain regions and generates a clinical reference report. This invention integrates multimodal brain MRI data from functional and structural magnetic resonance imaging to build a full-process disease prediction system. Through various algorithms, it mines the spatiotemporal dynamic characteristics of brain regions and integrates multimodal features to achieve dual-task prediction of Alzheimer's disease progression grading and brain age. Furthermore, through standardization processing and feature screening, it generates clinical reports to assist in disease diagnosis and treatment.

The invention discloses a multi-scale spatial information network classification method based on structural magnetic resonance imaging, and relates to the field of medical image processing. The problems that an existing Alzheimer's disease classification method based on sMRI is insufficient in focus detail information mining and poor in generalization ability are solved. The invention provides a multi-scale spatial information network classification method based on sMRI, and the method comprises the steps: taking sMRI as input data, extracting multi-scale frequency information through a multi-frequency perception fusion module based on three-dimensional sym2 wavelet transformation, and employing a graph mixed attention module to flexibly model a relationship between channels and a spatial position relationship, therefore, the mining capability and generalization performance of the model on sMRI focus detail information are improved. The method is also suitable for the fields of Alzheimer's disease image processing and structural magnetic resonance imaging-based multi-scale spatial information network classification equipment.

The application discloses a neural anatomical heterogeneity analysis method integrating standard model and non-negative matrix factorization, and belongs to the field of biomedical signal processing. The application comprises: preprocessing structural magnetic resonance imaging data of patients and corresponding healthy control groups to extract brain morphological indexes; establishing a brain morphological standard model of the patient through a transfer pre-training model to estimate a deviation score matrix of the patient's cortical thickness and surface area, and extracting a non-positive part to obtain a brain morphological atrophy deviation matrix; performing non-negative matrix factorization on the atrophy deviation matrix to generate a preliminary disease factor matrix and a weight composition matrix; and rearranging and sampling the deviation matrix, calculating an average half-decomposition stability coefficient and a reconstruction error to determine the disease factor and the weight composition under the optimal component number. The method of the application integrates spatial pattern information at the group level while retaining individual heterogeneity information, and provides a new technical means for precise diagnosis of neuropsychiatric diseases.

The invention relates to the field of neuroscience and ensemble learning, in particular to a brain age prediction method based on ensemble learning and structural magnetic resonance imaging, which comprises the following steps: collecting sMRI data and corresponding age information of a healthy adult subject; preprocessing the sMRI data, and randomly dividing the preprocessed sMRI data into a training set, a verification set and a test set; constructing a self-adaptive integrated learning model, and training the self-adaptive integrated learning model; inputting sMRI data to be predicted into the trained adaptive integrated learning model, and outputting a final brain age prediction result; and evaluating the final brain age prediction result through a mean absolute error, a root-mean-square error and a decision coefficient. Compared with the prior art, the method has the advantages that the brain age prediction precision and the model generalization ability can be improved, and the like.

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

The provided is a personalized target selection method for a non-invasive neuromodulation technology, including: preprocessing functional magnetic resonance imaging (fMRI) data from MRI scan data of a current patient to acquire fMRI brain image feature data; inputting the fMRI brain image feature data into a pre-trained inter-subtype classification model to acquire a subtype label of the current patient and all feature voxels of the subtype label; preprocessing T1-weighted MRI data of structural magnetic resonance imaging (sMRI) data from the MRI scan data of the current patient to acquire a skull outline and a transformation matrix between the sMRI and fMRI data; performing coordinate transformation on the feature voxels, calculating a distance between each voxel on the skull outline and each feature voxel, marking response feature voxels, and counting a number of response feature voxels; and sorting the number of response feature voxels.

A method and a device for localizing and lateralizing a brain functional region, an electronic apparatus, and a storage medium are provided herein, wherein the method for localizing and lateralizing the brain functional region comprises: acquiring brain structural magnetic resonance imaging data and brain functional magnetic resonance imaging data of a subject (201) based on the brain structural magnetic resonance imaging data and the brain functional magnetic resonance imaging data, determining a brain functional map of the subject (202), wherein the brain functional map comprises brain functional region identifiers of at least two brain functional regions and a corresponding set of voxels; for each of to-be-clinically-intervened voxels in a set of the to-be-clinically-intervened voxels, determining, based on the to-be-clinically-intervened voxel and the brain functional map, the brain functional region identifier corresponding to the to-be-clinically-intervened voxel (203); and determining a brain functional lateralization of the brain functional region of a target surgical region. Accurate and non-invasive preoperative localizing and lateralization of brain functional regions for individual subjects is achieved.

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 invention provides a brain-targeted neuron ion signal specificity bimodal nanoprobe and a preparation method and application thereof.The nanoprobe comprises mesoporous silicon nanoparticles, and a micromolecular magnetic resonance T1 contrast agent and a potassium ion indicator are loaded in pore channels of the mesoporous silicon nanoparticles; the surfaces of the meso-porous silicon nanoparticles are modified with potassium ion selective filter membranes and brain-targeted ligands. The neuron ion signal specificity fluorescence-magnetic resonance bimodal nanoprobe provided by the invention can effectively combine high-resolution structural magnetic resonance imaging with high-sensitivity functional fluorescence imaging, so that real-time dynamic high-resolution imaging of a neuron potassium ion signal is realized; the method has good research and application prospects in the aspects of precise positioning and operation guidance of the structure-hidden epilepsy focus.

The invention discloses a multi-modal brain function connection brain tumor postoperative post-traumatic stress disorder classification system and method. The system comprises a multi-modal data acquisition module, a multi-map brain network construction module, a multi-kernel map convolution feature extraction module, a dynamic prediction model construction module and an interpretability visualization module. Structural magnetic resonance, functional magnetic resonance and electroencephalogram multi-modal data are integrated, a functional connection network is constructed based on multi-graph division, and multi-scale features are extracted by using a multi-kernel graph convolutional network, so that high-precision PTSD classification and prediction are realized. The problems that existing brain tumor postoperative PTSD diagnosis depends on subjective evaluation and lacks objective biological markers are solved, an integrated solution of early recognition, individualized diagnosis and interpretable analysis is provided, and the diagnosis precision and clinical effectiveness of brain tumor postoperative mental complications are remarkably improved.

The present disclosure provides a structural magnetic resonance imaging analysis method, system and storage medium, including: obtaining sample data, wherein the sample data is a T1-weighted structural image obtained by structural magnetic resonance imaging, determining a map of morphological indicators of a region of interest in the original brain network space according to the sample data, resampling the map to a preset standard brain network space template, and performing Gaussian kernel smoothing to obtain standard brain network space template data, mapping the standard brain network space template data to a pre-constructed brain atlas template, determining the feature input of a machine learning model according to the mapped brain atlas template, and training the classification performance of the machine learning model based on the feature input to obtain a classification model. The effectiveness and reliability of the training can be improved, thereby improving the accuracy of classification based on the classification model obtained by training.

The invention discloses a medical image analysis method based on graph decoupling, and the method comprises the steps: fusing structure magnetic resonance imaging data and clinical features to construct an individualized multi-instance graph, and completing the priori-guided structure initialization through significance node detection and cross-sample consistency edge reservation, internal sub-graphs and false sub-graphs are separated through a differentiable binary mask and a double-path frame, and targeted loss constraints are applied respectively; a cross-sample intervention mechanism based on a prototype is introduced, two types of fusion graphs are constructed, corresponding losses are applied, core node discrimination is enhanced by combining the prototype comparison losses, masks and network parameters are updated by adopting an alternating optimization strategy, internal sub-graphs are extracted by utilizing the learned masks in the reasoning stage, and the trained graph neural network and a classifier are input to output a diagnosis result. The method achieves the precise separation of effective features and interference features, strengthens the consistency of cross-sample features, improves the diagnosis precision and robustness, and is suitable for medical image disease diagnosis scenes.