Visual quality assessment of MRI is subjective and impractical for large datasets. In this study, we present a cascaded convolutional neural network (CNN) model for automated image quality evaluation of structural brain MRI. The multisite Autism Brain Imaging Data Exchange dataset of ~1000 subjects was used to train and evaluate the proposed model. The model performance was compared with expert evaluation. The first network rated individual slices, and the second network combined the slice ratings into a final image score. The network achieved 74% accuracy, 69% sensitivity, and 74% specificity, demonstrating that deep learning can provide robust image quality evaluation.
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