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Three‐dimensional quantitative muscle ultrasound in a healthy population
Author(s) -
Jong Leon,
Nikolaev Anton,
Greco Anna,
Weijers Gert,
Korte Chris L.,
Fütterer Jurgen J.
Publication year - 2021
Publication title -
muscle and nerve
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.025
H-Index - 145
eISSN - 1097-4598
pISSN - 0148-639X
DOI - 10.1002/mus.27330
Subject(s) - ultrasound , kurtosis , tibialis anterior muscle , medicine , 3d ultrasound , rectus femoris muscle , population , nuclear medicine , biomedical engineering , radiology , skeletal muscle , anatomy , physical medicine and rehabilitation , electromyography , mathematics , statistics , environmental health
/Aims Quantitative muscle ultrasound offers biomarkers that aid in the diagnosis, detection, and follow‐up of neuromuscular disorders. At present, quantitative muscle ultrasound methods are 2D and are often operator and device dependent. The aim of this study was to combine an existing device independent method with an automated ultrasound machine and perform 3D quantitative muscle ultrasound, providing new normative data of healthy controls. Methods In total, 123 healthy volunteers were included. After physical examination, 3D ultrasound scans of the tibialis anterior muscle were acquired using an automated ultrasound scanner. Image postprocessing was performed to obtain calibrated echo intensity values based on a phantom reference. Results Tibialis anterior muscle volumes of 61.2 ± 24.1 mL and 53.7 ± 22.7 mL were scanned in males and females, respectively. Echo intensity correlated with gender**, age**, fat fraction*, histogram kurtosis**, skewness* and standard deviation** (* P < .05, ** P < .01). Outcome measures did not differ significantly for different acquisition presets. The 3D quantitative muscle ultrasound revealed the non‐uniformity of echo intensity values over the length of the tibialis anterior muscle. Discussion Our method extended 2D measurements and confirmed previous findings. Our method and reported normative data of (potential) biomarkers can be used to study neuromuscular disorders.