A supervised learning approach for diffusion MRI quality control with minimal training data

Quality control (QC) in diffusion-weighted MRI (DW-MRI) involves identifying problematic volumes in datasets. The current gold standard involves time-consuming manual inspection of data, and even supervised learning techniques that aim to replace the gold standard require manually labelled datasets for training. In this work we show the need for manual labelling can be greatly reduced by training a supervised classifier on realistic simulated data, and using a small amount of labelled data for a final calibration step. Such an approach may have applications in other image analysis tasks where labelled datasets are expensive or difficult to acquire.