We present an automated deep learning-based quality control system that generalizes to images of different orientations, images with and without contrast as well as those from different acquisition sites. Because the same model was able to classify images with different orientations, test-time augmentation substantially improved performance. Images that were moderately affected by artifacts were able to be identified with 95% accuracy. Furthermore, robustness to different data types (and potentially artifact types) was ensured by using an out-of-distribution detection procedure. This was able to discriminate spine MRI images from T1 brain images with an AUC of 0.98.