Abstract #3615

Relax-ADMM-Net: A Relaxed ADMM Network for Compressed Sensing MRI

Yiling Liu¹, Jing Cheng², Yanjie Zhu², Haifeng Wang², Ziwen Ke¹, Qiegen Liu³, Xin Liu², Hairong Zheng², Leslie Ying⁴, and Dong Liang^1,2

¹Research center for Medical AI, Shenzhen Institutes of Advanced Technology, shenzhen, China, ²Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, shenzhen, China, ³Department of Electronic Information Engineering, Nanchang University, Nanchang, China, ⁴Departments of Biomedical Engineering and Electrical Engineering, University at Buffalo,the State University of New York, Buffalo, NY, United States

ADMM is a popular algorithm for Compressed sensing (CS) MRI. ADMM-based deep networks have also achieved a great success by unrolling the ADMM algorithm into deep neural networks. Nevertheless, ADMM-Nets only make the components in the regularization term learnable. In this work, we propose a relaxed version of ADMM-Net (i.e. Relax-ADMM-Net) to further improve its performance for fast MRI, where the additional data consistency term and variable combinations in the updating rules are all freely learned by the network. Experiments reveal the effectiveness of the proposed network compared with several competing model-driven networks.

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