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

arXiv: https://arxiv.org/abs/2302.00528 · HTML: https://mlsanity.com/paper/2302.00528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles](https://mlsanity.com/paper/1903.12152/) (2019)
- [Coordinate Translator for Learning Deformable Medical Image Registration](https://mlsanity.com/paper/2203.03626/) (2022)
- [Disentangling A Single MR Modality](https://mlsanity.com/paper/2205.04982/) (2022)
- [Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment](https://mlsanity.com/paper/1809.09468/) (2018)

## Abstract (arXiv)

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.