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Abstract #0071

Learning a Variational Network for Reconstruction of Accelerated MRI Data

Kerstin Hammernik1,2, Erich Kobler1, Teresa Klatzer1, Michael P Recht2, Daniel K Sodickson2, Thomas Pock1,3, and Florian Knoll2

1Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 2Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU School of Medicine, New York, NY, United States, 3Safety & Security Department, AIT Austrian Institute of Technology GmbH, Vienna, Austria

In this work, we propose variational networks for fast and high-quality reconstruction of accelerated multi-coil MR data. A wide range of experiments and a dedicated user study on clinical patient data show that the proposed variational network reconstructions outperform traditional reconstruction approaches in terms of image quality and residual artifacts. Additionally, variational networks offer high reconstruction speed, which is substantial for the incorporation into clinical workflow.

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