Abstract #2800

Robust Magnetic Resonance Reconstruction by Alternating Deep Low-Rank Approach

Yihui Huang¹, Zi Wang¹, Xinlin Zhang², Meijin Lin³, Di Guo⁴, and Xiaobo Qu^1,5

¹Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, ²College of Physics and Information Engineering, Fuzhou University, Fuzhou, China, ³Department of Applied Marine Physics and Engineering, Xiamen University, Xiamen, China, ⁴School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ⁵Institute of Artificial Intelligence, Xiamen University, Xiamen, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction

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 model

Approach: 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.

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