Whole Brain Myelin Water Mapping in One Minute Using Tensor Dictionary Learning With Low-Rank Plus Sparse Regularization

The quantification of myelin water content in the brain can be obtained by the multi-echo T2* weighted images (T2*WIs). To accelerate the long acquisition, a novel tensor dictionary learning algorithm with low-rank and sparse regularization (TDLLS) is proposed to reconstruct the T2*WIs from the undersampled data. The proposed algorithm explores the local and nonlocal similarity and the global temporal redundancy in the real and imaginary parts of the complex relaxation signals. The joint application of the low-rank constraints on the dictionaries and the sparse constraints on the core coefficient tensors improves the performance of the tensor-based recovery. Parallel imaging is incorporated into the TDLLS algorithm (pTDLLS) for further acceleration. A pulse sequence is proposed to prospectively undersample the Ky-t space to obtain the whole brain high-quality myelin water fraction (MWF) maps within 1 minute at an undersampling rate (R) of 6.

Automated summary

A novel tensor dictionary learning algorithm, TDLLS, has been proposed to reconstruct myelin water content in the brain from undersampled T2* weighted images, improving the performance of tensor-based recovery. By incorporating low-rank constraints on the dictionaries and sparse constraints on the core coefficient tensors, the algorithm explores local and nonlocal similarity, and global temporal redundancy in the complex relaxation signals. Parallel imaging is applied for further acceleration, resulting in high-quality myelin water fraction maps obtained within 1 minute at an undersampling rate of 6.