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Abstract #4347

Low-rank Parallel Imaging Reconstruction Imbedded with a Deep Learning Prior Module

Linfang Xiao1,2, Yilong Liu1,2, Zheyuan Yi1,2, Yujiao Zhao1,2, Alex T.L. Leong1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China


Recently, deep learning methods have shown superior performance on image reconstruction and noise suppression by implicitly yet effectively learning prior information. However, end-to-end deep learning methods face the challenge of potential numerical instabilities and require complex application specific training. By taking advantage of the multichannel spatial encoding (as exploited by conventional parallel imaging reconstruction) and prior information (exploited by deep learning methods), we propose to embed a deep learning module into the iterative low-rank matrix completion based image reconstruction. Such strategy significantly suppresses the noise amplification and accelerates iteration convergence without image blurring.

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