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Abstract #4703

Multi-supervised Learning in Cross-domain Networks for Cardiac Imaging

Ziwen Ke1,2, Shanshan Wang2, Huitao Cheng1,2, Leslie Ying3, Xin Liu2, Hairong Zheng2, and Dong Liang1,2

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 Biomedical Engineering and Department of Electrical Engineering, The State University of New York, Buffalo, NY, United States

Dynamic MR image reconstruction from incomplete k-space data is an important technique for reducing its scan time. Deep learning has shown great potential in assisting this task. Nevertheless, most frameworks only adopt a final loss for network training and the intermediate results generated during the network forward pass haven't been considered for the network training. This work proposes a multi-supervised learning strategy, which constrains the frequency domain information and reconstruction results at different levels. Improved reconstruction results have been achieved with the proposed strategy.

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