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Abstract #2099

Large-scale image quality assessment using deep learning: impact of physiological factors and acquisition settings in whole-heart coronary MRA

Aurélien Maillot1,2, John Heerfordt1,2, Robin Demesmaeker1,3,4, Jonas Richiardi1, Dimitri Van De Ville3,5, Tobias Kober1,2,6, Juerg Schwitter7, Matthias Stuber2,8, and Davide Piccini 1,2,6

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Institute of Bioengineering/Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Institute of Electrical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 5Department of Radiology and Medical Informatics, University Hospital of Geneva (HUG), Geneva, Switzerland, 6LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 7Division of Cardiology and Cardiac MR Center, University Hospital of Lausanne (CHUV), Lausanne, Switzerland, 8Center for Biomedical Imaging (CIBM), Lausanne, Switzerland

Understanding which factors affect image quality is essential in order to perform high quality MRI acquisitions. Using a deep convolutional neural network, we performed automated Image Quality Assessment of 1102 heterogeneous whole-heart coronary MRA volumes acquired with a respiratory self-navigated ECG-triggered bSSFP sequence. A non-parametric multivariate rank regression was performed to predict image quality from available physiological and acquisition parameters. A large agreement between the Image Quality Scores (IQSs) estimated by the neural network and the fitted IQSs from the regression model was found (Spearman correlation 0.57). Gender, age, BMI, average RR interval, voxel size, trigger time and flip angle were found to be significant predictors of IQSs.

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