Zhejiang Lab

Haotian Qian

Yu Zhang

Zhichao Wang

Chaoliang Sun

Huan Zhang

Tianzi Jiang

Jingsong Li

Junyang Zhang

Wenyuan Qiu

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.

System for classifying working memory task magnetoencephalography based on machine learning

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.

Method and system for simulating magnetic resonance echo-planar imaging artifact

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.

System for precisely locating abnormal area of brain fiber bundle

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.

System for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging

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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Hangzhou City, China

Haotian Qian