Supervised multimodal brain image fusion method

Abstract

The invention discloses a supervised multimodal brain image fusion method comprising the following steps: S1, calculating the characteristic of each modal; S2, matrixing and normalizing the characteristics of the modals; S3, reducing the dimension of the modal characteristics using a singular value decomposition algorithm; S4, based on the dimension-reduced modal characteristics obtained in S3, maximizing the sum of squares of correlation between the typical variables of the modals and between the typical variables and prior information, and performing an iterative cycle until convergence; and S5, connecting the modal components obtained in S4 in series, and calculating out an independent component and a mixed matrix of each modal significantly associated with the prior information using a joint independent component analysis algorithm, thus realizing supervised multimodal brain image fusion. According to the embodiment of the invention, the method has good robustness and can reveal the physiological and pathological mechanism of complex brain disease cognitive impairment.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of medical image processing, and in particular to a supervised multimodal brain image fusion method. Background technique [0002] In recent years, the use of a variety of non-invasive imaging techniques (functional magnetic resonance imaging fMRI, structural magnetic resonance imaging sMRI, diffusion tensor imaging (DTI, etc.) to collect data of different modalities on the same subject has been widely adopted by researchers. A modality is simply the variety of imaging a single MRI machine can achieve. Each modality reflects the function or structure of the brain from different angles. For example, fMRI is based on blood oxygen level dependent (BOLD) signals to reflect the neuronal activity of the corresponding brain region when the brain is doing a certain task or in a resting state; sMRI provides information on the organizational structure of the brain: Gray matter (GM), white matte...

AI Technical Summary

Problems solved by technology

At present, a large part of the research on patient's symptom score or specific cognitive domain score is clinical score and drug evaluation. The research related to human brain magnetic resonance imaging, especially multimodal magnetic resonance imaging, is very limited, and most of them are based on independent analysis. Then solve the correlation, and research based on supervised learning to explore the relationship between multimodal neuroimaging (including magnetic resonance imaging, EEG, magnetoencephalogram) and specific clinical indicators has not yet been carried out

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