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

• URL: http://arxiv.org/abs/2111.03780v1
• Date: Sat, 6 Nov 2021 02:17:12 GMT
• Title: Artifact- and content-specific quality assessment for MRI with image rulers
• Authors: Ke Lei, John M. Pauly, Shreyas S. Vasanawala
• Abstract summary: 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. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers.
• Score: 11.551528894727573
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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