Multi-echo gradient-echo (mGRE)-based myelin water fraction (MWF) mapping is increasingly used for studying myelin integrity. The basic multi-exponential fitting method often suffers from severe ill-conditionedness of the exponential model. To address this problem, a number of more advanced estimation methods have been proposed, incorporating a priori constraints and machine learning. This work presents a new learning-based method to further improve MWF estimation. The proposed method represents different water components as low-rank subspaces through which both pre-learned subspace and manifold structures are synergistically integrated. Both simulation and experimental results demonstrate significantly improved performance over existing MWF estimation methods.
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