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Abstract #3501

VIOLIN: improVIng mOdel-based deep learning reconstruction by reLaxing varIable combiNations

Jing Cheng1, Yiling Liu1, Qiegen Liu2, Ziwen Ke1, Haifeng Wang1, Yanjie Zhu1, Leslie Ying3, Xin Liu1, Hairong Zheng1, and Dong Liang1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Nanchang University, Nanchang, China, 3University at Buffalo, The State University of New York, Buffalo, Buffalo, NY, United States

Most of the unrolling-based deep learning fast MR imaging methods learn the parameters and regularization functions with the network architecture structured by the corresponding optimization algorithm. In this work, we introduce an effective strategy, VIOLIN and use the primal dual hybrid gradient (PDHG) algorithm as an example to demonstrate improved performance of the unrolled networks via breaking the variable combinations in the algorithm. Experiments on in vivo MR data demonstrate that the proposed strategy achieves superior reconstructions from highly undersampled k-space data.

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