Multi-modal Brain Atlas Construction via Hypergraph Transition Matrix
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Solution Overview
Problem
Current brain atlases generated from single-modal image data lack feature information, leading to inaccurate disease diagnosis in brain diseases, particularly for complex conditions like Alzheimer's.
Innovation Solution
An image-driven brain atlas construction method that transforms node feature matrices into hypergraph data structures, generating a multi-modal brain atlas by calculating a hypergraph transition matrix, which integrates structural and functional connection information, using algorithms like Guth-Katz polynomial clustering and KNN, and iterative hypergraph edge neuron processing to enhance feature extraction and model generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If single-modal image data is used to generate brain atlases, then the construction process is simple, but the feature information expression is insufficient
Solution Approach 1:
The patent combines multiple modalities of brain imaging data (structural MRI, functional MRI, diffusion tensor imaging) into a unified multi-modal brain atlas. This merging approach integrates diverse feature information from different data sources, resolving the contradiction by prioritizing information completeness while managing complexity through systematic data fusion procedures
2Reliability
If multi-modal data is integrated to improve feature information, then diagnostic accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent introduces a standardized data processing framework that acts as an intermediary between multi-modal data sources and diagnostic analysis. This framework includes unified preprocessing protocols, feature extraction methods, and integration algorithms that manage the complexity of processing diverse data types while preserving diagnostic accuracy
Solution Approach 2:
The patent transforms multi-modal brain imaging data into standardized parameter representations through consistent preprocessing and feature extraction. By converting different data types into comparable parameter formats, the system manages processing complexity while maintaining the rich feature information necessary for accurate diagnosis
3Measurement precision
If comprehensive feature extraction is performed, then diagnostic precision improves, but computational time increases
Solution Approach 1:
The patent performs preliminary preprocessing and feature extraction on multi-modal brain imaging data during the atlas construction phase. By pre-processing the data in advance and creating a standardized multi-modal brain atlas, the system reduces the computational burden during actual diagnostic applications, thus improving detection precision without excessive processing time delays
Data Source
AI summary
The present application provides an image-driven brain atlas construction method and apparatus, a device and a storage medium, and involves in the field of medical imaging technologies. The method includes: acquiring a node feature matrix, where the node feature matrix includes time sequences of multiple nodes of a brain; performing hypergraph data structure transformation on the node feature matrix to acquire a first hypergraph incidence matrix; inputting the first hypergraph incidence matrix and the node feature matrix into a trained hypergraph transition matrix generator for processing to output and acquire a first hypergraph transition matrix, where the first hypergraph transition matrix characterizes a constructed multi-modal brain atlas. The technical solution provided by the present application can construct the multi-modal brain atlas, and this multi-modal brain connection structure can express more feature information. When it is applied to the brain disease diagnosis process, the accuracy of disease diagnosis can be improved.