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Abstract #4783

Deep Learning and Compressed Sensing: Automated Image Quality Assessment in Iterative Respiratory-Resolved Whole-Heart MRI

Robin Demesmaeker1,2, Jérôme Yerly3,4, John Heerfordt1,3, Tobias Kober1,3,5, Pier Giorgio Masci6, Dimitri Van De Ville2,7, Matthias Stuber3,4, and Davide Piccini1,3,5

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Institute of Bioengineering/Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 3Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 4Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 5LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 6Division of Cardiology and Cardiac MR Center, University Hospital of Lausanne (CHUV), Lausanne, Switzerland, 7Department of Radiology and Medical Informatics, University Hospital of Geneva (HUG), Geneva, Switzerland

We aim at creating a link between compressed sensing (CS) reconstruction and automated image quality (IQ) assessment using deep learning. An automated image quality assessment algorithm based on a deep convolutional neural regression network trained to evaluate the quality of whole-heart MRI datasets is used to assess IQ at every iteration of a respiratory motion-resolved CS reconstruction. Not only IQ evolution as assessed by the network visually correlates with the CS cost function, but the neural network is able to distinguish the image quality of different respiratory phases with high correlation to visual expert assessment.

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