Oranchuk Dustin J, Boncella Katie L, Gonzalez-Rivera Daniella, Harris-Love Michael O
Muscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Physical Medicine and Rehabilitation, University of Colorado, Anschutz Medical Campus, Aurora, Colorado.
Muscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Bioengineering, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado.
Eur J Transl Myol. 2025 Jun 27;35(2). doi: 10.4081/ejtm.2025.13511. Epub 2025 Apr 1.
Cost-effective and portable ultrasonography offers a promising approach for monitoring skeletal muscle damage and quality in many contexts. However, echogenicity analysis relies on precise transducer orientations and machine parameters, posing challenges for data pooling across different raters and settings. Muscle texture analysis offers a potential means of reducing inter-rater and machine-setting variability. Scans were assessed at nine angles, controlled using a custom transducer shell and software. Scans were performed three times, and different gains were applied. All scans were performed on a muscle tissue-mimicking phantom to eliminate biological variability. Intra-angle and intra-gain variability and internal consistency were assessed via coefficient of variation (CV%) and Cronbach's alpha (αc). Spearman's (ρ) correlations were employed to determine the relationship between echogenicity and each texture feature. Entropy (angle: CV=2.7-7.6%; gain: CV=10.5%; αc=0.86), and inverse difference moment (angle: CV=3.7-9.8%; gain: CV=16.5%; αc=0.87) were less variable than echogenicity (angle: CV=6.4-19.4%; gain: CV=39.0%; αc=0.82). Angular second moment (angle: CV=17.9-116.6%; gain: CV=71.6%; αc=0.68), contrast (angle: CV=7.8-14.7%; gain: CV=41.8%;αc=0.75), and correlation (angle: CV=9.0-13.5%; gain: CV=28.6%; αc=0.49) features were generally more variable. Entropy (ρ=0.82-0.98, p≤0.011) and inverse difference moment (ρ=-0.98--0.83, p≤0.008), were more strongly correlated with echogenicity than angular second moment (ρ=-0.98--0.77, p≤0.016), contrast (ρ=0.53-0.98, p≤0.15), and correlation (ρ=-0.25--0.19, p=0.520-0.631). Entropy and inverse difference moment features may allow data sharing between laboratory and clinical settings with ultrasound machine parameters and raters of varying skill levels. Clinical and mechanistic studies are required to determine if texture features can replace echogenicity assessments.
性价比高且便携的超声检查在许多情况下为监测骨骼肌损伤和质量提供了一种很有前景的方法。然而，回声分析依赖于精确的换能器方向和机器参数，这给不同评估者和不同设置下的数据汇总带来了挑战。肌肉纹理分析提供了一种减少评估者间和机器设置差异的潜在方法。使用定制的换能器外壳和软件控制，在九个角度对扫描进行评估。扫描进行了三次，并应用了不同的增益。所有扫描均在模拟肌肉组织的体模上进行，以消除生物变异性。通过变异系数（CV%）和克朗巴哈系数（αc）评估角度内和增益内的变异性以及内部一致性。采用斯皮尔曼相关系数（ρ）来确定回声性与每个纹理特征之间的关系。熵（角度：CV = 2.7 - 7.6%；增益：CV = 10.5%；αc = 0.86）和逆差矩（角度：CV = 3.7 - 9.8%；增益：CV = 16.5%；αc = 0.87）的变异性低于回声性（角度：CV = 6.4 - 19.4%；增益：CV = 39.0%；αc = 0.82）。角二阶矩（角度：CV = 17.9 - 116.6%；增益：CV = 71.6%；αc = 0.68）、对比度（角度：CV = 7.8 - 14.7%；增益：CV = 41.8%；αc = 0.75）和相关性（角度：CV = 9.0 - 13.5%；增益：CV = 28.6%；αc = 0.49）特征通常变异性更大。熵（ρ = 0.82 - 0.98，p≤0.011）和逆差矩（ρ = -0.98 - -0.83，p≤0.008）与回声性的相关性比角二阶矩（ρ = -0.98 - -0.77，p≤0.016）、对比度（ρ = 0.53 - 0.98，p≤0.15）和相关性（ρ = -0.25 - -0.19，p = 0.520 - 0.631）更强。熵和逆差矩特征可能允许在实验室和临床环境之间共享数据，而不受超声机器参数和不同技能水平评估者的影响。需要进行临床和机制研究来确定纹理特征是否可以取代回声性评估。
