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Abstract #4666

A Cascaded Residual Dense Network for Cardiac MR Imaging

Ziwen Ke1,2, Shanshan Wang2, Cheng Li2, 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

Cardiac MR imaging plays an important role in clinical diagnosis. But the long scan time limits its wide applications. To accelerate data acquisition, deep learning based methods have been applied to effectively reconstruct the undersampled images. However, current deep convolutional neural network (CNN) based methods do not make full use of the hierarchical features from different convolutional layers, which impedes their performances. In this work, we propose a cascaded residual dense network (C-RDN) for dynamic MR image reconstruction with both local features and global features being fully explored. Our proposed C-RDN achieves the best performance on in vivo datasets compared to the iterative optimization methods and the state-of-the-art CNN method.

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