  Image quality control (IQC) can be used in automated magnetic resonance (MR)
image analysis to exclude erroneous results caused by poorly acquired or
artifact-laden images. Existing IQC methods for MR imaging generally require
human effort to craft meaningful features or label large datasets for
supervised training. The involvement of human labor can be burdensome and
biased, as labeling MR images based on their quality is a subjective task. In
this paper, we propose an automatic IQC method that evaluates the extent of
artifacts in MR images without supervision. In particular, we design an
artifact encoding network that learns representations of artifacts based on
contrastive learning. We then use a normalizing flow to estimate the density of
learned representations for unsupervised classification. Our experiments on
large-scale multi-cohort MR datasets show that the proposed method accurately
detects images with high levels of artifacts, which can inform downstream
analysis tasks about potentially flawed data.
