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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

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

摘要

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.