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Abstract #2815

Accelerating Quantification of Myelin Water Fraction with Nonlocal Low-Rank Tensor in the Feature Domain

Quan Chen1, Huajun She1, and Yiping P. Du1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Acceleration of myelin water fraction (MWF) mapping using the Feature domain nonlocal Low-Rank Tensor based (FnLRT) algorithm is investigated in this study. The global temporal information of the whole images is used to project the T2* weighted images (T2*WIs) into the feature domain. The nonlocal and local spatial redundancies in the feature domain are further exploited. The tensor-based decomposition is used to explore the multi-dimensional redundancies. The human brain experiments demonstrate the outperformance of the FnLRT algorithm over the state-of-the-art reconstructions at R=6. The FnLRT algorithm provides the potential to obtain the whole brain MWF mapping in 1 minute.

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