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Abstract #3368

Parallel Imaging Reconstruction with a Conditional Generative Adversarial Network

Pengyue Zhang1,2, Fusheng Wang1, and Yu Li2,3

1Department of Computer Science, Stony Brook University, Stony Brook, NY, United States, 2Department of Cardiac Imaging, St.Francis Hospital, Greenvale, NY, United States, 3Department of Radiology, Stony Brook University, Stony Brook, NY, United States

This work presents a parallel imaging reconstruction framework based on deep neural networks. A conditional generative adversarial network (conditional GAN) is used to learn how to recover anatomical image structure from undersampled data for imaging acceleration. The new approach is shown to be suitable for image reconstruction with high undersampling factors when conventional parallel imaging suffers from a g-factor increase.

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