Abstract #1088

Learning a Variational Model for Compressed Sensing MRI Reconstruction

Kerstin Hammernik¹, Florian Knoll², Daniel K Sodickson², and Thomas Pock^1,3

¹Institute for 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

Compressed sensing techniques allow MRI reconstruction from undersampled k-space data. However, most reconstruction methods suffer from high computational costs, selection of adequate regularizers and are limited to low acceleration factors for non-dynamic 2D imaging protocols. In this work, we propose a novel and efficient approach to overcome these limitations by learning a sequence of optimal regularizers that removes typical undersampling artifacts while keeping important details in the imaged objects and preserving the natural appearance of anatomical structures. We test our approach on patient data and show that we achieve superior results than commonly used reconstruction methods.

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