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Abstract #4712

Edge-enhanced Loss Constraint for Deep Learning Based Dynamic MR Imaging

Shanshan Wang1, Ziwen Ke1,2, Huitao Cheng1,2, Leslie Ying3, Xin Liu1, Hairong Zheng1, and Dong Liang1,2

1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China, 2Research center for Medical AI, 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 magnetic resonance (MR) imaging provides a powerful imaging tool for clinical diagnosis. However, due to the constraints of magnetic resonance (MR) physics and reconstruction algorithms, dynamic MR imaging takes a long time to scan. Recently, deep learning has achieved preliminary success in MR reconstruction. Compared with the classical iterative optimization algorithms, the deep learning based methods can get improved reconstruction results in shorter time. However, most current deep convolutional neural network (CNN) based methods use mean square error (MSE) as the loss function, which might be a reason for image smooth in the reconstruction. In this work, we propose to employ edge-enhanced constraint for loss function and explore different types of total variation on network training. Encouraging performances have been achieved.

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