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.