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公开(公告)号：US20240393417A1
公开(公告)日：2024-11-28
申请号：US18796233
申请日：2024-08-06
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Zhichao WANG , Chaoliang SUN , Huan ZHANG , Haotian QIAN , Junyang ZHANG , Tianzi JIANG
IPC: G01R33/561 , G01R33/56 , G01R33/565
Abstract: A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts. A simulation algorithm is designed according to the principle of generation of EPI sequence artifacts, and the obtained images such as stripe artifacts, Moer artifacts, Nyquist artifacts, susceptibility artifacts and the like have good scientificity, accuracy and interpretability.
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公开(公告)号：US20230225649A1
公开(公告)日：2023-07-20
申请号：US18125645
申请日：2023-03-23
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Chaoliang SUN , Zhichao WANG , Haotian QIAN , Jun LI , Jingsong LI
CPC classification number: A61B5/16 , A61B5/7264
Abstract: Disclosed is a graph model-based brain functional alignment method. The method includes: mapping high-dimensional functional brain imaging data to a two-dimensional time-series matrix by taking brain functional activity signals of a subject under a specific cognitive function state as input , constructing a model based on graph convolutional networks to distinguish different cognitive function states, generating a brain activation distribution priori graph by a meta analysis method to assist in predicting a specific brain function activation mode of each subject, combining the two to map functional brain imaging data of each subject to a shared representation space applicable to a large-scale group, and finally achieving accurate brain function alignment between subjects. According to the method, graph representation information generated in the shared representation space can also be used for accurately predicting the brain function state and behavioral index of the subjects.
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Patent Agency Ranking
公开(公告)号：US20240389880A1
公开(公告)日：2024-11-28
申请号：US18796264
申请日：2024-08-06
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Chaoliang SUN , Zhichao WANG , Huan ZHANG , Haotian QIAN , Tianzi JIANG
Abstract: A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.
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4.发明申请公开(公告)号：US20240398305A1
公开(公告)日：2024-12-05
申请号：US18798861
申请日：2024-08-09
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Haotian QIAN , Chaoliang SUN , Zhichao WANG , Huan ZHANG , Tianzi JIANG
Abstract: A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.
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5.发明公开公开(公告)号：US20230290514A1
公开(公告)日：2023-09-14
申请号：US18129754
申请日：2023-03-31
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Jun LI , Chaoliang SUN , Huan ZHANG , Zhichao WANG , Jingsong LI
CPC classification number: G16H50/30 , G06T7/0012 , G06V10/62 , G06F17/16 , G06T2207/10088 , G06T2207/30016
Abstract: Disclosed are a disease prediction method, system and apparatus based on a multi-relation functional connectivity matrix. A Pearson correlation coefficient matrix and a DTW distance matrix are respectively calculated according to resting state functional magnetic resonance time series extracted from a brain atlas, the DTW distance matrix is converted in combination with the Pearson correlation coefficient matrix into a DTW′ matrix which includes correlation degree and correlation direction information and whose numerical range is equivalent to the value range of a Pearson coefficient, and a functional connectivity matrix is obtained after weighted combination. The present disclosure combines DTW distance information to weaken the dynamic change of functional connectivity and the influence of asynchrony of functional signals in different brain regions on the functional connectivity matrix, so that the calculated functional connectivity matrix can better reflect the correlation between the functional signals in different brain regions.
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公开(公告)号：US20240394882A1
公开(公告)日：2024-11-28
申请号：US18796239
申请日：2024-08-06
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Chaoliang SUN , Zhichao WANG , Huan ZHANG , Haotian QIAN , Tianzi JIANG
Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method. The system can better integrate the cross-modal physiological indexes of multiple brain regions and the correlation between brain regions and improve prediction ability of the model and generalization ability of the model with different diseases.
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7.发明公开公开(公告)号：US20230301542A1
公开(公告)日：2023-09-28
申请号：US18125640
申请日：2023-03-23
Applicant: ZHEJIANG LAB
Inventor： Yu ZHANG , Wenyuan QIU , Zhichao WANG , Chaoliang SUN , Haotian QIAN , Jingsong LI
CPC classification number: A61B5/055 , G06T7/0012 , G06T2207/30016 , G06T2207/10088 , G06T2207/20084
Abstract: The present disclosure discloses a brain atlas individualization method and system based on magnetic resonance and a twin graph neural network. Firstly, a feature is extracted from resting-state functional magnetic resonance imaging (rs-fMRI) by utilizing functional connectivity based on a region-of-interest, and at the same time, Fisher transformation and exponential transformation are performed on the feature; secondly, a corresponding adjacent matrix is extracted from T1-weighted magnetic resonance data in a data set; and then the twin graph neural network is designed for training and testing with the transformed feature and the adjacent matrix as inputs and a group atlas label and a sampling mask as outputs.
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