Brain map individualization method and system based on magnetic resonance and twin map neural network

A neural network and brain map technology, applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve the problems of slow convergence of iterative schemes and long reconstruction time, and achieve the goals of reducing reconstruction time, improving variance, and ensuring consistency Effect

Active Publication Date: 2022-04-22
ZHEJIANG LAB
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Problems solved by technology

The above scheme still has some disadvantages, such as only considering the characteristics of a single fMRI data node or its first-order domain node, and its iterative scheme converges slowly and takes a long time to reconstruct

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  • Brain map individualization method and system based on magnetic resonance and twin map neural network
  • Brain map individualization method and system based on magnetic resonance and twin map neural network
  • Brain map individualization method and system based on magnetic resonance and twin map neural network

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Embodiment Construction

[0038] The specific embodiments of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0039] The present invention provides a brain atlas individualization method and system based on magnetic resonance and twin map neural network, which can express individual differences as much as possible while ensuring the consistency of functional areas of subjects. Construct functional connectivity and adjacency matrix based on the subject's rs-fMRI data and T1-weighted MRI data as model input; use the group map to design a sampling mask, obtain a region with high consistency as the sampling region, and introduce the two into the loss function; design a reconstruction model based on the twin graph neural network, and train; finally, since the brain map has no real value as an evaluation, select some indicators that can evaluate the rationality of the individual brain map, such as evaluating the individual brain map in the task s...

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Abstract

The invention discloses a brain map individualization method and system based on magnetic resonance and a twin map neural network. The method comprises the following steps: firstly, extracting features from resting state functional magnetic resonance data (rs-fMRI) by using functional connection based on a region of interest, and meanwhile, carrying out Fisher transform and exponential transform on the features; secondly, extracting a corresponding adjacent matrix from the T1 weighted magnetic resonance data in the data set; and then, taking the transformed features and the adjacent matrix as input, taking a group map label and a sampling mask as output, and designing a twin map neural network for training and testing. Compared with other rs-fMRI individualized map schemes, the activation distribution of the individualized brain map reconstructed by a twin network architecture and a center sampling mode designed by utilizing the data characteristics of rs-fMRI and a group map is more uniform on the task state magnetic resonance data, and the reconstruction time is shorter.

Description

technical field [0001] The present invention relates to the fields of medical imaging and deep learning, in particular to a method and system for individualizing brain maps based on magnetic resonance and twin map neural networks. Background technique [0002] The cerebral cortex provides an important basis for human perception, movement, and other cognitive functions. In neuroscience, brain division is of great significance for the cognition, location and analysis of brain function. [0003] The regions of the cerebral cortex exhibit different functions, structures, connections, and topologies, and thus can be classified by clustering. At present, there are quite a few non-invasive methods to obtain relevant information in the cerebral cortex, such as magnetic resonance (MRI), magnetoencephalography (MEG), and so on. Among them, MRI has become a widely used imaging technique due to its advantages such as relatively high temporal and spatial resolution and no radiation. R...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/055A61B5/245A61B5/00
CPCA61B5/055A61B5/245A61B5/7264A61B5/7267
Inventor 张瑜邱文渊李军陈子洋孙超良李劲松
Owner ZHEJIANG LAB
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