Abstract #4783

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

Robin Demesmaeker^1,2, Jérôme Yerly^3,4, John Heerfordt^1,3, Tobias Kober^1,3,5, Pier Giorgio Masci⁶, Dimitri Van De Ville^2,7, Matthias Stuber^3,4, and Davide Piccini^1,3,5

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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