A latent space for unsupervised MR image quality control via artifact assessment
This addresses the need for efficient and unbiased quality assessment in MR imaging, reducing reliance on subjective human labeling, though it is incremental as it builds on existing unsupervised techniques.
The paper tackled the problem of automating magnetic resonance (MR) image quality control to detect artifacts without human supervision, and the result was a method that accurately identifies images with high artifact levels on large-scale datasets.
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.