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Panoramic ultrasound vs. MRI for the assessment of hamstrings cross-sectional area and volume in a large athletic cohort

Abstract We investigated the validity of panoramic ultrasound (US) compared to magnetic resonance imaging (MRI) for the assessment of hamstrings cross-sectional area (CSA) and volume. Hamstrings CSA were acquired with US (by an expert operator) at four different sites of femur length (FL) in 85 yout...

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Bibliographic Details
Published in:Scientific reports 2020-08, Vol.10 (1), p.14144-14144, Article 14144
Main Authors: Franchi, Martino V., Fitze, Daniel P., Hanimann, Jonas, Sarto, Fabio, Spörri, Jörg
Format: Article
Language:English
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Summary:Abstract We investigated the validity of panoramic ultrasound (US) compared to magnetic resonance imaging (MRI) for the assessment of hamstrings cross-sectional area (CSA) and volume. Hamstrings CSA were acquired with US (by an expert operator) at four different sites of femur length (FL) in 85 youth competitive alpine skiers (14.8 ± 0.5 years), and successively compared to corresponding scans obtained by MRI, analyzed by a trained vs. a novice rater. The agreement between techniques was assessed by Bland–Altman analyses. Statistical analysis was carried out using Pearson’s product moment correlation coefficient ( r ). US-derived CSA showed a very good agreement compared to MRI-based ones. The best sites were 40% FL (0 = mid patellar point) for biceps femoris long head (r = 0.9), 50% for semitendinosus (r = 0.9), and 30% for semimembranosus (r = 0.86) and biceps femoris short head (BFsh, r = 0.8). US-based vs. MRI-based hamstrings volume showed an r of 0.96. Poorer r values were observed for the novice compared to the trained rater, with the biggest difference observed for BFsh at 50% (r = 0.001 vs. r = 0.50, respectively) and semimembranosus at 60% (r = 0.23 vs. r = 0.42, respectively). Panoramic US provides valid CSA values and volume estimations compared to MRI. To ensure optimal US-vs.-MRI agreement, raters should preferably possess previous experience in imaging-based analyses.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-71123-6