Concordia University, Department Health Kinesiology and Applied Physiology, Montreal, Quebec, Canada.
Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada; PERFORM Centre, Concordia University, Montreal, Quebec, Canada.
Musculoskelet Sci Pract. 2023 Feb;63:102717. doi: 10.1016/j.msksp.2023.102717. Epub 2023 Jan 5.
The aim of this observational cross-sectional study was to examine correlations of intramuscular fat content in lumbar multifidus (LM) by comparing muscle echo intensity (EI) and percent fat signal fraction (%FSF) generated from ultrasound (US) and magnetic resonance (MR) images, respectively.
MRI and US images from 25 participants (16 females, 9 males) selected from a cohort of patients with chronic low back pain (CLBP) were used. Images were acquired bilaterally, at the L4 and L5 levels (e.g., 4 sites). EI measurements were acquired by manually tracing the cross-sectional border of LM. Mean EI of three US images per site were analyzed (e.g., raw EI). A correction factor for subcutaneous fat thickness (SFT) was also calculated and applied (e.g., corrected EI). Corresponding fat and water MR images were used to acquire %FSF measurements. Intra-rater reliability was assessed by intraclass coefficients (ICC). Pearson correlations and simple linear regression were used to assess the relationship between %FSF, raw EI and corrected EI measurements.
The intra-rater ICCs for all measurements were moderate to excellent. Correlations between %FSF vs. raw EI and corrected EI were moderate to strong (0.40 < r < 0.52) and (0.40 < r < 0.51), respectively. Moderate correlations between SFT and EI were also identified.
US is a low-cost, non-invasive, accessible, and reliable method to examine muscle composition, and presents a promising solution for assessing and monitoring the effect of different treatment options for CLBP in clinical settings.
本观察性横断面研究旨在通过比较超声(US)和磁共振(MR)图像分别生成的腰椎多裂肌(LM)内肌肉回声强度(EI)和脂肪信号分数百分比(%FSF),来检测 LM 内的肌肉脂肪含量相关性。
从慢性下腰痛(CLBP)患者队列中选择了 25 名参与者(16 名女性,9 名男性)的 MRI 和 US 图像。在 L4 和 L5 水平(例如,4 个部位)分别进行双侧图像采集。通过手动追踪 LM 的横截面边界获取 EI 测量值。对每个部位的 3 张 US 图像的平均 EI 进行分析(例如,原始 EI)。还计算并应用了皮下脂肪厚度(SFT)的校正因子(例如,校正 EI)。使用相应的脂肪和水 MR 图像获取 %FSF 测量值。通过组内相关系数(ICC)评估内部评估者的可靠性。使用 Pearson 相关和简单线性回归评估 %FSF、原始 EI 和校正 EI 测量值之间的关系。
所有测量的内部评估者 ICC 均为中等至极好。%FSF 与原始 EI 和校正 EI 之间的相关性为中等至强(0.40<r<0.52)和(0.40<r<0.51),SFT 与 EI 之间也存在中度相关性。
US 是一种低成本、非侵入性、易于获得和可靠的方法,可用于检查肌肉成分,并为评估和监测 CLBP 患者不同治疗方案的效果提供了有前景的解决方案。