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Abstract #4786

POCS Augmented CycleGAN for MR Image Reconstruction

Hanlu Yang1, Yiran Li1, Danfeng Xie1, and Wang Ze2

1Electrical & Computer Engineering Department, Temple University, Philadelphia, PA, United States, 2Department of Radiology, Temple University, Philadelphia, PA, United States

Traditional MRI reconstruction depends heavily on solving nonlinear optimization problems, which could be highly time-consuming and sensitive to noise. We proposed a hybrid DL-based MR image reconstruction method by combining two state-of-art deep learning networks, U-Net and CycleGAN (Generative adversarial network with cycle loss) and a traditional method: projection onto convex set (POCS). Our result shows a high reconstruction accuracy and this method can be further used to increase the sample size, which may find many applications in situations where the training samples are limited such as medical images.

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