公开(公告)号：US12223650B2

公开(公告)日：2025-02-11

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

IPC: G06T7/00 ,  G16H30/40

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.

公开(公告)号：US12127826B2

公开(公告)日：2024-10-29

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

IPC: A61B5/055 ,  G01R33/48 ,  G06N3/042 ,  G06T7/00

CPC classification number: A61B5/055 ,  G01R33/4806 ,  G06N3/042 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20084 ,  G06T2207/30016

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.