Abstract #2099

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

Aurélien Maillot^1,2, John Heerfordt^1,2, Robin Demesmaeker^1,3,4, Jonas Richiardi¹, Dimitri Van De Ville^3,5, Tobias Kober^1,2,6, Juerg Schwitter⁷, Matthias Stuber^2,8, 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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