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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)
Verlagsversion DOI PMC
Open Access Hybrid
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Artifacts ; Data Quality ; Magnetic Resonance Imaging ; Metrics ; Motion; Robust
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0968-5243
e-ISSN 1352-8661
Zeitschrift Magnetic Resonance Materials in Physics, Biology and Medicine
Verlag Springer
Verlagsort One New York Plaza, Suite 4600, New York, Ny, United States
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen Elsass Fonden
DOI 10.1007/s10334-025-01266-y
WOS ID 001505273800001
Scopus ID 105007775031
PubMed ID 40493331
Erfassungsdatum 2025-06-25
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