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

A latent space for unsupervised MR image quality control via artifact assessment