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

IPC: G16H50/30 ,  G06T7/00 ,  G06V10/62 ,  G06F17/16

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.

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

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：US20240311300A1

公开(公告)日：2024-09-19

申请号：US18470346

申请日：2023-09-19

Applicant: ZHEJIANG LAB ,  Huazhong University of Science and Technology

Inventor： Zhan ZHANG ,  Yu ZHANG ,  Jin ZHAO ,  Haifei WU

IPC: G06F12/0804

CPC classification number: G06F12/0804 ,  G06F2212/1032

Abstract: The present disclosure provides a data storage system, including data cache module, data processing module, and a persistent memory. The data cache module includes an on-chip mapping data cache and an on-chip counter cache, where the mapping data cache is configured to cache mapping data, and when the free space of the mapping data cache is less than a preset threshold, the least recently used mapping data cache line will be evicted from the cache and written back to the persistent memory. The data processing module encrypts/decrypts persistent memory data by using their counters, and accesses the persistent memory blocks indicated by their corresponding mapping data. The persistent memory comprises the first and second storage regions for the latest checkpoint data and modified working data in the current checkpoint interval respectively.

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

IPC: A61B5/055 ,  G06T7/00

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.