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

Synopsis

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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