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Abstract #4784

Deep Learning for Automated Medical Image Quality Assessment: Proof of Concept in Whole-Heart Magnetic Resonance Imaging

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

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, 4LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 5Center for Biomedical Imaging (CIBM), 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 developing a fully automated algorithm which quantitatively gauges the quality of medical images using deep learning to mimic human perception. An automated image quality assessment algorithm based on a deep convolutional neural regression network is designed, optimized, trained, validated and tested on a clinical database of 3D whole-heart cardiac MRI scans. The algorithm was successfully trained and validated, yielding a regression performance in the range of the intra- and inter-observer agreement. These results show the relevance of deep learning concepts to image quality analysis, in particular to volumetric cardiac MR imaging.

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