Support vector machine image classification is performed on MR brain images to determine the need to repeat the MR acquisition. However, the image feature extraction is completely brain image agnostic. It is performed either directly on image slices or simple transformations thereof, like e.g. by fore/background thresholding or 1-level wavelet decomposition. 120 image features and meta-data entries are used to classify images as sufficient to diagnose or not. 84% accuracy is demonstrated, even after reducing the feature space to only 20 features. Such feature computation is fast enough to perform image quality assessment in real time, immediately after scan completion.