  In this paper, we introduce an image quality assessment (IQA) method for
pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise
using a nonlocal residual neural network (NR-Net) and then volume-wise by
agglomerating the slice QA results using random forest. Our method requires
only a small amount of quality-annotated images for training and is designed to
be robust to annotation noise that might occur due to rater errors and the
inevitable mix of good and bad slices in an image volume. Using a small set of
quality-assessed images, we pre-train NR-Net to annotate each image slice with
an initial quality rating (i.e., pass, questionable, fail), which we then
refine by semi-supervised learning and iterative self-training. Experimental
results demonstrate that our method, trained using only samples of modest size,
exhibit great generalizability, capable of real-time (milliseconds per volume)
large-scale IQA with near-perfect accuracy.
