Reconstruction of sparsely sampled Magnetic Resonance Imaging measurements with a convolutional neural network

Jasper Schoormans, Qinwei Zhang, Bram Coolen, Gustav Strijkers, Aart Nederveen

Apr 11, 2018 (modified: May 16, 2018) MIDL 2018 Abstract Submission readers: everyone
  • Abstract: Compressed Sensing accelerated Magnetic Resonance Imaging (MRI) suffers from long image reconstruction times, due to the need for solving ill-posed minimizations. This limits the clinical use of accelerated MRI techniques. We have trained a neural network to decode accelerated, undersampled MR acquisitions, eliminating the need for reconstruction algorithms.
  • Author affiliation: Academic Medical Center Amsterdam
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