Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS)

作者：Mani Merry; Jacob Mathews^*; Kelley Douglas; Magnotta Vincent

来源：Magnetic Resonance in Medicine, 2017, 78(2): 494-507.

DOI：10.1002/mrm.26382

• structured low-rank
• annihilating filter
• multi-shot diffusion
• calibration-less
• motion compensation
• regularized recovery

PurposeTo introduce a novel method for the recovery of multi-shot diffusion weighted (MS-DW) images from echo-planar imaging (EPI) acquisitions.
MethodsCurrent EPI-based MS-DW reconstruction methods rely on the explicit estimation of the motion-induced phase maps to recover artifact-free images. In the new formulation, the k-space data of the artifact-free DWI is recovered using a structured low-rank matrix completion scheme, which does not require explicit estimation of the phase maps. The structured matrix is obtained as the lifting of the multi-shot data. The smooth phase-modulations between shots manifest as null-space vectors of this matrix, which implies that the structured matrix is low-rank. The missing entries of the structured matrix are filled in using a nuclear-norm minimization algorithm subject to the data-consistency. The formulation enables the natural introduction of smoothness regularization, thus enabling implicit motion-compensated recovery of the MS-DW data.
ResultsOur experiments on in-vivo data show effective removal of artifacts arising from inter-shot motion using the proposed method. The method is shown to achieve better reconstruction than the conventional phase-based methods.
ConclusionWe demonstrate the utility of the proposed method to effectively recover artifact-free images from Cartesian fully/under-sampled and partial Fourier acquired data without the use of explicit phase estimates. Magn Reson Med 78:494-507, 2017.

• 出版日期2017-8