Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI

Quantitative assessments of paraspinal muscles and their relationship with lumbar extensor muscle function based on Dixon magnetic resonance imaging techniques

BackgroundThe dysfunction of paraspinal muscles is closely associated with degenerative spine disorders and the development of low-back pain. Currently, methods for evaluating paraspinal muscle function mainly focus on anatomical imaging and functional assessment.ObjectiveThis study aimed to explore the quantitative assessments of paraspinal muscles and their relationship with the strength and endurance of lumbar extensor muscles based on magnetic resonance imaging with Dixon techniques.MethodsFifty-four volunteers aged 45 years and older were recruited from our outpatient clinic. The participants underwent 3.0-T Dixon magnetic resonance imaging of the lumbar region. The Dixon sequence was used for measuring the cross-sectional area (CSA) and fat infiltration (FI) of paraspinal muscles (multifidus, erector spinae, and psoas major) at the L1-S1 level. The strength and endurance of lumbar extensor muscles were assessed using a standing external fixation testing bracket. Pearson or Spearman coefficients were used to evaluate the relationship between the quantitative assessment indicators of paraspinal muscle degeneration and the strength and endurance of lumbar extensor muscles (corrected for body height [BH] and weight [BW]).ResultsAt the L1-2 level, multifidus FI negatively correlated with extensor strength (ES), ES/BH, extensor endurance (EE), EE/BH, and EE/BW (r = -0.286, -0.269, -0.317, -0.306, -0.281; P < 0.05), and erector spinae FI negatively correlated with EE, EE/BH, and EE/BW (r = -0.315, -0.293, -0.268; P < 0.05). At the L2-3 level, multifidus FI negatively correlated with EE, EE/BH, and EE/BW (r = -0.358, -0.347, -0.327; P < 0.05), and erector spinae FI negatively correlated with EE, EE/BH, and EE/BW (r = -0.334, -0.310, -0.283; P < 0.05). At the L3-4 level, multifidus FI negatively correlated with EE (r = -0.271, P < 0.05), and psoas major CSA negatively correlated with ES/BW (r = -0.299, P < 0.05). At the L4-5 level, multifidus FI negatively correlated with EE and EE/BH (r = -0.286, -0.268; P < 0.05). At the L5-S1 level, multifidus FI negatively correlated with EE, EE/BH, and EE/BW (r = -0.418, -0.404, -0.377; P < 0.05).ConclusionThe FI of multifidus at the L5-S1 level may reflect the endurance level of extensor muscles to some extent. The FI of paraspinal muscles is relatively better than CSA in predicting the strength and endurance of lumbar extensor muscles. Proper extensor muscle functional exercises may slow down the process of paraspinal muscle FI to some extent.

Myosteatosis: diagnostic significance and assessment by imaging approaches

Myosteatosis has emerged as an important concept in muscle health as it is associated with an increased risk of adverse health outcomes, a higher rate of complications, and increased mortality associated with ageing, chronic systemic and neuromuscular diseases, cancer, metabolic syndromes, degenerative events, and trauma. Myosteatosis involves ectopic infiltration of fat into skeletal muscle, and it exhibits a negative correlation with muscle mass, strength, and mobility representing a contributing factor to decreased muscle quality. While myosteatosis serves as an additional biomarker for sarcopenia, cachexia, and metabolic syndromes, it is not synonymous with sarcopenia. Myosteatosis induces proinflammatory changes that contribute to decreased muscle function, compromise mitochondrial function, and increase inflammatory response in muscles. Imaging techniques such as computed tomography (CT), particularly opportunistic abdominal CT scans, and magnetic resonance imaging (MRI) or magnetic resonance spectroscopy (MRS), have been used in both clinical practice and research. And in recent years, ultrasound has emerged as a promising bedside tool for measuring changes in muscle tissue. Various techniques, including CT-based muscle attenuation (MA) and intermuscular adipose tissue (IMAT) quantification, MRI-based proton density fat fraction (PDFF) and T1-T2 mapping, and musculoskeletal ultrasound (MSUS)-based echo intensity (EI) and shear wave elastography (SWE), are accessible in clinical practice and can be used as adjunct biomarkers of myosteatosis to assess various debilitating muscle health conditions. However, a stan¬dard definition of myosteatosis with a thorough understanding of the pathophysiological mechanisms, and a consensus in assessment methods and clinical outcomes has not yet been established. Recent developments in image acquisition and quantification have attempted to develop an appropriate muscle quality index for the assessment of myosteatosis. Additionally, emerging studies on artificial intelligence (AI) may provide further insights into quantification and automated assessment, including MRS analysis. In this review, we discuss the pathophysiological aspects of myosteatosis, all the current imaging techniques and recent advances in imaging assessment as potential biomarkers of myosteatosis, and the most common clinical conditions involved.

Quantifying lumbar paraspinal intramuscular fat: Accuracy and reliability of automated thresholding models

Background

The reported level of lumbar paraspinal intramuscular fat (IMF) in people with low back pain (LBP) varies considerably across studies using conventional T1- and T2-weighted magnetic resonance imaging (MRI) sequences. This may be due to the different thresholding models employed to quantify IMF. In this study we investigated the accuracy and reliability of established (two-component) and novel (three-component) thresholding models to measure lumbar paraspinal IMF from T2-weighted MRI.

Methods

In this cross-sectional study, we included MRI scans from 30 people with LBP (50% female; mean (SD) age: 46.3 (15.0) years). Gaussian mixture modelling (GMM) and K-means clustering were used to quantify IMF bilaterally from the lumbar multifidus, erector spinae, and psoas major using two and three-component thresholding approaches (GMM2C; K-means2C; GMM3C; and K-means3C). Dixon fat-water MRI was used as the reference for IMF. Accuracy was measured using Bland-Altman analyses, and reliability was measured using ICC3,1. The mean absolute error between thresholding models was compared using repeated-measures ANOVA and post-hoc paired sample t-tests (α = 0.05).

Results

We found poor reliability for K-means2C (ICC3,1 ≤ 0.38), moderate to good reliability for K-means3C (ICC3,1 ≥ 0.68), moderate reliability for GMM2C (ICC3,1 ≥ 0.63) and good reliability for GMM3C (ICC3,1 ≥ 0.77). The GMM (p < .001) and three-component models (p < .001) had smaller mean absolute errors than K-means and two-component models, respectively. None of the investigated models adequately quantified IMF for psoas major (ICC3,1 ≤ 0.01).

Conclusions

The performance of automated thresholding models is strongly dependent on the choice of algorithms, number of components, and muscle assessed. Compared to Dixon MRI, the GMM performed better than K-means and three-component performed better than two-component models for quantifying lumbar multifidus and erector spinae IMF. None of the investigated models accurately quantified IMF for psoas major. Future research is needed to investigate the performance of thresholding models in a more heterogeneous clinical dataset and across different sites and vendors.

Artificial Intelligence in Spine and Paraspinal Muscle Analysis

Disorders affecting the neurological and musculoskeletal systems represent international health burdens. A significant impediment to progress with interventional trials is the absence of responsive, objective, and valid outcome measures sensitive to early disease or disorder change. A key finding in individuals with spinal disorders is compositional changes to the paraspinal muscle and soft tissue (e.g., intervertebral disc, facet joint capsule, and ligamentous) structure. Quantification of paraspinal muscle composition by MRI has emerged as a sensitive marker for the severity of these conditions; however, little is known about the composition of muscles across the lifespan. Knowledge of what is "typical" age-related muscle composition is essential in order to accurately identify and evaluate "atypical," with a potential impact being improvements in pre- and postsurgical plan and measurement of surgical implants, exoskeletons, and care on a patient-by-patient basis.