# Advances in mechanical assessments of in vivo human lumbar spine tissues with noninvasive imaging techniques

**Authors:** Dawn M. Elliott, Harrah R. Newman, Mackenzie N. Conner, Curtis L. Johnson, Edward J. Vresilovic

PMC · DOI: 10.1038/s44385-026-00070-0 · Npj Biomedical Innovations · 2026-02-18

## TL;DR

This paper reviews noninvasive imaging techniques that assess spinal tissue mechanics in living humans, aiming to improve understanding and treatment of low back pain.

## Contribution

The paper introduces emerging noninvasive imaging methods for in vivo spinal tissue mechanics, offering new diagnostic and therapeutic opportunities.

## Key findings

- Noninvasive imaging techniques can quantify spinal tissue mechanics beyond static anatomy.
- Technical progress is hindered by variability and challenges in distinguishing age-related changes from pathology.
- Integration with computational models and machine learning could enhance clinical impact.

## Abstract

Low back pain (LBP) is the leading cause of disability worldwide, yet clinical imaging remains largely limited to anatomical assessment, providing little insight into the spinal tissue mechanics underlying most idiopathic cases. This review highlights emerging noninvasive imaging technologies that enable in vivo quantification of intervertebral disc and spinal muscle mechanics, including radiography, ultrasound imaging, ultrasound elastography, magnetic resonance imaging, and magnetic resonance elastography. These approaches move beyond static morphology to capture spinal kinematics, load-dependent deformation, and tissue material properties under physiologically relevant conditions. Despite substantial technical progress, translation is hindered by inter-individual variability, limited symptomatic cohorts, and challenges in separating age-related changes from pathology. We discuss opportunities to accelerate clinical impact through development of normative mechanical datasets, dynamic and load-dependent imaging paradigms, and integration of imaging-derived mechanical biomarkers with computational modeling and machine learning. Together, these innovations position mechanics-based imaging to enable objective diagnosis, improved patient stratification, and mechanism-driven treatment of low back pain.

## Full-text entities

- **Diseases:** reduced multifidus (MESH:D001523), spondylolisthesis (MESH:D013168), loss of disc height (MESH:C000719188), tumors (MESH:D009369), liver fibrosis (MESH:D008103), spinal stenosis (MESH:D013130), atrophy (MESH:D001284), Disease (MESH:D004194), muscle atrophy (MESH:D009133), fatty (MESH:D008067), fracture (MESH:D050723), pain (MESH:D010146), stenosis (MESH:D003251), disc herniation (MESH:D007405), LBP (MESH:D017116), Neck pain (MESH:D019547), fatty infiltration (MESH:D017254), fatigue (MESH:D005221), carcinogenesis (MESH:D063646), vertebral fracture (MESH:C535781), sacroiliac joint pain (MESH:D018771), scoliosis (MESH:D012600), scoliotic (MESH:C536198), stiffness (MESH:C566112), neuromuscular (MESH:D009468), spinal instability (MESH:D043171), disability (MESH:D009069), brain tumors (MESH:D001932), chronic (MESH:D002908), bony abnormalities (MESH:D018213), paraspinal muscle degeneration (MESH:D009410), musculoskeletal condition (MESH:D009140), AF (OMIM:614822), Disc degeneration (MESH:D055959), disc pathology (MESH:D005598), lordosis (MESH:D008141), dysfunction (MESH:D006331)
- **Chemicals:** water (MESH:D014867)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

4 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12916484/full.md

## References

23 references — full list in the complete paper: https://tomesphere.com/paper/PMC12916484/full.md

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Source: https://tomesphere.com/paper/PMC12916484