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Abstract #2972

Accelerating Myelin Water Content Quantification using Deep Non-Local Sparse Model

Bowen Li1, Huajun She1, Quan Chen2, Zhijun Wang1, and Yiping P. Du1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Radiology, Stanford University, Stanford, CA, United States


A grouping sparse coding (Group-SC) model is used in this study for the acceleration of myelin water content quantification. Images with improved quality are obtained using the Group-SC algorithm, in the aspect of minimal artefacts and good data consistency with fully-sampled labels. Myelin water fraction (MWF) maps reconstructed using Group-SC demonstrate more natural spatial distribution of myelin water in the brain. The proposed Group-SC algorithm has demonstrated its potential for the acceleration of the myelin water content quantification at R = 6.

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