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

Lianrui Zuo; Yuan Xue; Blake E. Dewey; Yihao Liu; Jerry L. Prince; Aaron Carass

doi:10.1117/12.2652917

3 April 2023 A latent space for unsupervised MR image quality control via artifact assessment

Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, Aaron Carass

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246418 (2023) https://doi.org/10.1117/12.2652917
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, and Aaron Carass "A latent space for unsupervised MR image quality control via artifact assessment", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246418 (3 April 2023); https://doi.org/10.1117/12.2652917

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