Robust Magnetic Resonance Reconstruction by Alternating Deep Low-Rank Approach

Motivation: Magnetic resonance reconstruction by deep learning is heavily compromised due to the mismatch between the training and target data, such as the sampling rate of undersampling, the organ and the contrast of imaging. Goal(s): Reliablely reconstruct magnetic resonance signal in multiple scenes by one trained deep learning modelApproach: Alternating Deep Low-Rank, which combines deep learning solvers and classic low-rank optimization solvers. Results: Compared with state-of-the-art deep learning methods HDSLR and ODLS, one ADLR trained by coronal PDw knee can provide a lower reconstruction error by about 10% in coronal PDw knees, 15% in sagittal PDw knees, and 30% in axial T2w brains. Impact: The proposed ADLR can effectively alleviate the drop in reconstruction quality due to the mismatches of attributes between training and target signals of the MR imaging or MR spectroscopy.