Muscle Morphology and Imaging in Chronic Low Back Pain

Chronic low back pain (CLBP) imposes a heavy global burden and is increasingly understood as not only a symptom of spinal degeneration but also a manifestation of altered muscle morphology. Paraspinal muscles—including the multifidus, erector spinae and psoas major—play a vital role in the biomechanical support and stability of the lumbar spine. In CLBP, these muscles often exhibit decreased cross‐sectional area and elevated fat infiltration, changes detectable by advanced imaging techniques. Magnetic resonance imaging enables detailed assessment of muscle volume, composition and architecture. Emerging approaches such as fat–water Dixon sequences, manual and automated segmentation, and computer‐vision models allow quantification of intramuscular fat and muscle-to-fat ratios. These morphological markers have been linked to spinal–pelvic alignment, surgical outcomes and functional capacity. Studies integrating imaging with sagittal-balance analysis reveal that muscle degeneration may both reflect and contribute to lumbar lordosis alterations and pelvic tilt discrepancies. Moreover, large-scale automated analysis of paraspinal muscle health underscores the influence of age, sex, body mass index and activity levels on muscle composition, refining interpretation in patients with acute or chronic pain. The integration of imaging biomarkers into clinical pathways promises more accurate patient stratification, tailored rehabilitation and surgical planning, while ongoing refinements in segmentation and modelling seek to harmonise assessment protocols and enhance predictive value.

Research from Nature Portfolio

Recent studies have detailed the relationship between paravertebral muscle degeneration and spinal–pelvic sagittal parameters in patients with lumbar disc herniation. Quantitative magnetic resonance imaging revealed that higher fat infiltration and reduced muscle area in multifidus and erector spinae correlate with decreased lumbar lordosis, increased pelvic tilt and altered sagittal vertical axis. These findings suggest that muscle composition not only mirrors disc pathology but may influence global spinal alignment, underlining the importance of incorporating muscle assessment into routine imaging protocols for degenerative lumbar conditions.

Muscle Morphology and Imaging in Chronic Low Back Pain publication trend

The graph below shows the total number of articles in muscle morphology and imaging in chronic low back pain across all publications each year (not limited to Nature Index journals).

Technical terms

Paraspinal muscles: The group of muscles adjacent to the spine, including multifidus, erector spinae and psoas major, responsible for maintaining posture and spinal stability.

Cross-sectional area (CSA): The two-dimensional area of a muscle on an axial imaging slice, indicative of muscle size and potential strength.

Intramuscular fat (IMF): Fat content within muscle tissue measured by imaging techniques, reflecting degeneration or deconditioning.

Relative cross-sectional area (RCSA): CSA normalised to vertebral or body size, allowing comparison across individuals.

Fat infiltration: The process by which adipose tissue accumulates within muscle fibres, often associated with disuse or degeneration.

Spinal–pelvic sagittal parameters: Angular measures such as lumbar lordosis, pelvic tilt and sagittal vertical axis, describing the alignment of spine and pelvis in the sagittal plane.

References

1. Advances in the interaction between lumbar intervertebral disc degeneration and fat infiltration of paraspinal muscles: critical summarization, classification, and perspectives. Frontiers in Endocrinology (2024).
2. The Cross-Sectional Area Assessment of Pelvic Muscles Using the MRI Manual Segmentation among Patients with Low Back Pain and Healthy Subjects. Journal of Imaging (2023).
3. Relationship between paravertebral muscle degeneration and spinal-pelvic sagittal parameters in patients with lumbar disc herniation. Scientific Reports (2024).
4. Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: a UK Biobank study. The Spine Journal (2024).