Artifact- and content-specific quality assessment for MRI with image rulers

TL;DR

This paper introduces a multi-task CNN framework with calibrated labels and image rulers for artifact- and content-specific MRI quality assessment, enabling more accurate and interpretable evaluations tailored to different diagnostic needs.

Contribution

It presents a novel approach combining calibrated human labels, image rulers, and multi-task learning for MRI quality assessment focused on noise and motion artifacts.

Findings

Achieves around 90% accuracy, outperforming previous methods.

Outperforms human experts by 3% in noise assessment.

Label calibration and image rulers enhance model performance and generalizability.

Abstract

In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.