An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images

Savannah Hays, Lianrui Zuo, Blake E. Dewey, Samuel Remedios, Jinwei Zhang, Ellen M. Mowry, Scott D. Newsome, Aaron Carass, Jerry L Prince

Keywords: MRI, Quality Assurance, Artifact Detection

TL;DR: We introduce an unsupervised QA framework that quantifies MR image artifact severity for automated quality assessment.

Abstract: Quality assurance (QA) in magnetic resonance (MR) imaging is critical but remains a challenging and time-intensive process, particularly when working with large-scale, multi-site imaging datasets. Manual QA methods are subjective, prone to inter-rater variability, and impractical for high-throughput workflows. Existing automated QA methods often lack generalizability to diverse datasets or fail to provide interpretable insights into the causes of poor image quality. To address these limitations, we introduce an unsupervised and interpretable QA framework for multi-contrast MR images that quantifies artifact severity. By assigning a numerical score to each image, our method enables objective, consistent evaluation of image quality and highlights specific levels of artifact presence that can impair downstream analysis. Our framework employs an unsupervised contrastive learning approach, leveraging simulated artifact transformations, including random bias, noise, anisotropy, and ghosting, to train the model without requiring manual labels or preprocessing. A margin-based contrastive loss further enables differentiation between varying levels of artifact severity. We validate our framework using simulated artifacts on a public dataset and real artifacts on a private clinical dataset, demonstrating its robustness and generalizability for automatic MR image QA. By efficiently evaluating image quality and identifying artifacts prior to data processing, our approach streamlines QA workflows and enhances the reliability of subsequent analyses in both research and clinical settings.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Interpretability and Explainable AI

Paper Type: Methodological Development

Registration Requirement: Yes

Reproducibility: https://github.com/shays15/artifact_scoring

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Submission Number: 187