PDF(Pubmed)
Abstract：
UNASSIGNED: Lumbar paraspinal muscles (LPM) are a part of the deep spinal stabilisation system and play an important role in stabilising the lumbar spine and trunk. Inadequate function of these muscles is thought to be an essential aetiological factor in low back pain, and several neuromuscular diseases are characterised by dysfunction of LPM. The main aims of our study were to develop a methodology for LPM assessment using advanced magnetic resonance imaging (MRI) methods, including a manual segmentation process, to confirm the measurement reliability, to evaluate the LPM morphological parameters [fat fraction (FF), total muscle volume (TMV) and functional muscle volume (FMV)] in a healthy population, to study the influence of physiological factors on muscle morphology, and to build equations to predict LPM morphological parameters in a healthy population.
UNASSIGNED: This prospective cross-sectional observational comparative single-centre study was conducted at the University Hospital in Brno, enrolling healthy volunteers from April 2021 to March 2023. MRI of the lumbar spine and LPM (erector spinae muscle and multifidus muscle) were performed using a 6-point Dixon gradient echo sequence. The segmentation of the LPM and the control muscle (psoas muscle) was done manually to obtain FF and TMV in a range from Th12/L1 to L5/S1. Intra-rater and inter-rater reliability were evaluated. Linear regression models were constructed to assess the effect of physiological factors on muscle FF, TMV and FMV.
UNASSIGNED: We enrolled 90 healthy volunteers (median age 38 years, 45 men). The creation of segmentation masks and the assessment of FF and TMV proved reliable (Dice coefficient 84% to 99%, intraclass correlation coefficient ≥0.97). The univariable models showed that FF of LPM was influenced the most by age (39.6% to 44.8% of variability, P<0.001); TMV and FMV by subject weight (34.9% to 67.6% of variability, P<0.001) and sex (24.7% to 64.1% of variability, P<0.001). Multivariable linear regression models for FF of LPM included age, body mass index and sex, with R-squared values ranging from 45.4% to 51.1%. Models for volumes of LPM included weight, age and sex, with R-squared values ranged from 37.4% to 76.8%. Equations were developed to calculate predicted FF, TMV and FMV for each muscle.
UNASSIGNED: A reliable methodology has been developed to assess the morphological parameters (biomarkers) of the LPM. The morphological parameters of the LPM are significantly influenced by physiological factors. Equations were constructed to calculate the predicted FF, TMV and FMV of individual muscles in relation to anthropometric parameters, age, and sex. This study, which presented LPM assessment methodology and predicted values of LPM morphological parameters in a healthy population, could improve our understanding of diseases involving LPM (low back pain and some neuromuscular diseases).
UNASSIGNED: This prospective cross-sectional observational comparative single-centre study was conducted at the University Hospital in Brno, enrolling healthy volunteers from April 2021 to March 2023. MRI of the lumbar spine and LPM (erector spinae muscle and multifidus muscle) were performed using a 6-point Dixon gradient echo sequence. The segmentation of the LPM and the control muscle (psoas muscle) was done manually to obtain FF and TMV in a range from Th12/L1 to L5/S1. Intra-rater and inter-rater reliability were evaluated. Linear regression models were constructed to assess the effect of physiological factors on muscle FF, TMV and FMV.
UNASSIGNED: We enrolled 90 healthy volunteers (median age 38 years, 45 men). The creation of segmentation masks and the assessment of FF and TMV proved reliable (Dice coefficient 84% to 99%, intraclass correlation coefficient ≥0.97). The univariable models showed that FF of LPM was influenced the most by age (39.6% to 44.8% of variability, P<0.001); TMV and FMV by subject weight (34.9% to 67.6% of variability, P<0.001) and sex (24.7% to 64.1% of variability, P<0.001). Multivariable linear regression models for FF of LPM included age, body mass index and sex, with R-squared values ranging from 45.4% to 51.1%. Models for volumes of LPM included weight, age and sex, with R-squared values ranged from 37.4% to 76.8%. Equations were developed to calculate predicted FF, TMV and FMV for each muscle.
UNASSIGNED: A reliable methodology has been developed to assess the morphological parameters (biomarkers) of the LPM. The morphological parameters of the LPM are significantly influenced by physiological factors. Equations were constructed to calculate the predicted FF, TMV and FMV of individual muscles in relation to anthropometric parameters, age, and sex. This study, which presented LPM assessment methodology and predicted values of LPM morphological parameters in a healthy population, could improve our understanding of diseases involving LPM (low back pain and some neuromuscular diseases).
摘要：
■腰椎旁肌肉（LPM）是深层脊柱稳定系统的一部分，在稳定腰椎和躯干方面发挥着重要作用。这些肌肉的功能不足被认为是下腰痛的重要病因，和一些神经肌肉疾病的特征是LPM的功能障碍。我们研究的主要目的是开发一种使用先进的磁共振成像（MRI）方法进行LPM评估的方法。包括手动分割过程，为了确认测量的可靠性，评估LPM形态参数[脂肪分数（FF），健康人群的总肌肉体积(TMV)和功能性肌肉体积(FMV)]，研究生理因素对肌肉形态的影响,并建立方程来预测健康人群的LPM形态参数。
■这项前瞻性横断面观察性比较单中心研究是在布尔诺大学医院进行的，从2021年4月至2023年3月招募健康志愿者。使用6点Dixon梯度回波序列进行腰椎和LPM（竖脊肌和多裂肌）的MRI。手动进行LPM和对照肌肉(腰大肌)的分割,以获得在Th12/L1至L5/S1范围内的FF和TMV。评估者内部和评估者之间的可靠性。构建线性回归模型评估生理因素对肌肉FF的影响,TMV和FMV。
■我们招募了90名健康志愿者（平均年龄38岁，45名男子)。分割掩模的创建以及FF和TMV的评估被证明是可靠的(Dice系数84%至99%，组内相关系数≥0.97)。单变量模型表明，LPM的FF受年龄影响最大（变异性的39.6％至44.8％，P<0.001）；TMV和FMV按受试者体重（变异性的34.9％至67.6％，P<0.001）和性别（变异性的24.7%至64.1%，P<0.001)。LPMFF的多变量线性回归模型包括年龄，体重指数和性别，R平方值范围为45.4%至51.1%。LPM体积模型包括重量，年龄和性别,R平方值范围为37.4%至76.8%。开发了方程来计算预测的FF，每个肌肉的TMV和FMV。
■已经开发了一种可靠的方法来评估LPM的形态参数（生物标志物）。LPM的形态参数受生理因素的显著影响。构造方程来计算预测的FF，个体肌肉的TMV和FMV与人体测量参数的关系，年龄,和性爱。这项研究,提出了健康人群LPM评估方法和LPM形态参数预测值，可以提高我们对涉及LPM（下腰痛和一些神经肌肉疾病）的疾病的认识。
■这项前瞻性横断面观察性比较单中心研究是在布尔诺大学医院进行的，从2021年4月至2023年3月招募健康志愿者。使用6点Dixon梯度回波序列进行腰椎和LPM（竖脊肌和多裂肌）的MRI。手动进行LPM和对照肌肉(腰大肌)的分割,以获得在Th12/L1至L5/S1范围内的FF和TMV。评估者内部和评估者之间的可靠性。构建线性回归模型评估生理因素对肌肉FF的影响,TMV和FMV。
■我们招募了90名健康志愿者（平均年龄38岁，45名男子)。分割掩模的创建以及FF和TMV的评估被证明是可靠的(Dice系数84%至99%，组内相关系数≥0.97)。单变量模型表明，LPM的FF受年龄影响最大（变异性的39.6％至44.8％，P<0.001）；TMV和FMV按受试者体重（变异性的34.9％至67.6％，P<0.001）和性别（变异性的24.7%至64.1%，P<0.001)。LPMFF的多变量线性回归模型包括年龄，体重指数和性别，R平方值范围为45.4%至51.1%。LPM体积模型包括重量，年龄和性别,R平方值范围为37.4%至76.8%。开发了方程来计算预测的FF，每个肌肉的TMV和FMV。
■已经开发了一种可靠的方法来评估LPM的形态参数（生物标志物）。LPM的形态参数受生理因素的显著影响。构造方程来计算预测的FF，个体肌肉的TMV和FMV与人体测量参数的关系，年龄,和性爱。这项研究,提出了健康人群LPM评估方法和LPM形态参数预测值，可以提高我们对涉及LPM（下腰痛和一些神经肌肉疾病）的疾病的认识。
