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

TL;DR

This paper introduces an unsupervised method for MR image quality control that uses contrastive learning and normalizing flows to detect artifacts without human-labeled data, improving automation and objectivity.

Contribution

It presents a novel artifact encoding network combined with normalizing flows for unsupervised MR image quality assessment, eliminating the need for labeled datasets.

Findings

Accurately detects images with high artifact levels

Works effectively on large-scale multi-cohort datasets

Reduces reliance on human labeling for quality control

Abstract

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.