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Abstract #2786

Investigation of convolutional neural network based deep learning for cardiac imaging

Shanshan Wang1, Ziwen Ke1, Huitao Cheng1, Leslie Ying2, Xin Liu1, Hairong Zheng1, and Dong Liang1

1Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, China, 2Department of Biomedical Engineering and Department of Electrical Engineering, The State University of New York, Buffalo, New York, Armenia

Deep learning based fast MR imaging has been very popular lately. Nevertheless, the empirical nature of existing approaches still leave quite a few questions open. To address this, this paper designs different convolutional neural networks to investigate various factors, such as direct CNN mapping, noise stimulation, data consistency and data sharing, for deep learning based cardiac imaging. We find out that if K-space manipulation strategy is not adopted, CNN still needs dedicated sampling patterns or more complicated structures to remove global corruptions. Furthermore, K-space updating strategy are encouraged to be incorporated with deep learning for better final performances.

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