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Abstract #1091

Variational Adversarial Networks for Accelerated MR Image Reconstruction

Kerstin Hammernik1,2, Erich Kobler1, Thomas Pock1,3, Michael P Recht2, Daniel K Sodickson2, and Florian Knoll2

1Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 2Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, 3Safety & Security Department, AIT Austrian Institute of Technology GmbH, Vienna, Austria

Inspired by variational networks and adversarial training, we introduce variational adversarial networks for accelerated MR image reconstruction to overcome typical limitations of using simple image quality measures as loss functions for training. While simple loss functions, such as mean-squared-error and structural similarity index, result in low resolution and blurry images, we show that the proposed variational adversarial network leads to sharper images and preserves fine details for clinical low and high SNR patient data.

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