Marchetto, Elisa, Eichhorn, Hannah, Gallichan, Daniel  ORCID: https://orcid.org/0000-0002-0143-2855, Schnabel, Julia A. and Ganz, Melanie
2025.
Agreement of image quality metrics with radiological evaluation in the presence of motion artifacts.
Magnetic Resonance Materials in Physics, Biology and Medicine
10.1007/s10334-025-01266-y
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Abstract
Objective: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. We compare the performance of common reference-based and reference-free image quality metrics on unique datasets with real motion artifacts, and analyze the metrics’ robustness to typical pre-processing techniques. Materials and methods: We compared five reference-based and five reference-free metrics on brain data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and radiological evaluation. Results: All reference-based metrics showed strong correlation with observer assessments. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. The strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Discussion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing significantly influences correlation values. Future research should focus on refining pre-processing techniques and exploring approaches for automated image quality evaluation.
| Item Type: | Article |
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| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Publisher: | Springer |
| ISSN: | 1352-8661 |
| Funders: | Copenhagen University |
| Date of First Compliant Deposit: | 27 June 2025 |
| Date of Acceptance: | 14 May 2025 |
| Last Modified: | 03 Jul 2025 10:19 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/179352 |
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