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Abstract #4775

Learning Primal Dual Network for Fast MR Imaging

Jing Cheng1, Haifeng Wang1, Leslie Ying2, and Dong Liang1,3

1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institude of Advanced Technoleoy,Chinese Academy of Sciences, Shenzhen, China, 2Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, United States, 3Research Center for Medical AI, Shenzhen Institude of Advanced Technoleoy, Chinese Academy of Sciences, Shenzhen, China

We introduce a novel deep learning network which combines elements of model and data driven approaches for fast MR imaging, termed modified Learned PD. The network is inspired by the first-order primal dual algorithm, where the convolutional neural network blocks are used to learn the proximal operators. Learned PD network works directly from undersampled k-space data and reconstructs MR images by updating in k-space and image domain alternatively. This approach has been evaluated by in vivo MR datasets and achieves accurate MR reconstructions, outperforming other comparing methods across various quantitative metrics.

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