End-to-End Variational Networks for Accelerated MRI Reconstruction

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Authors Patricia Johnson, C. Lawrence Zitnick, Anuroop Sriram, Florian Knoll, Aaron Defazio, Tullie Murrell, Jure Zbontar, Nafissa Yakubova
Journal/Conference Name Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Paper Category
Paper Abstract The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). While the combination of these methods has the potential to allow much faster scan times, reconstruction from such undersampled multi-coil data has remained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset for both brain and knee MRIs.
Date of publication 2020
Code Programming Language Python

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