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

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.