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Marchetto, E.* ; Eichhorn, H. ; Gallichan, D.* ; Schnabel, J.A. ; Ganz, M.*

Agreement of image quality metrics with radiological evaluation in the presence of motion artifacts.

Magn. Reson. Mater. Phys. Biol. Med., DOI: 10.1007/s10334-025-01266-y (2025)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Artifacts ; Data Quality ; Magnetic Resonance Imaging ; Metrics ; Motion; Robust
ISSN (print) / ISBN 0968-5243
e-ISSN 1352-8661
Journal Magnetic Resonance Materials in Physics, Biology and Medicine
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Grants Elsass Fonden