Prediction of magnetic resonance imaging-derived trunk muscle geometry with application to spine biomechanical modeling

Jaejin Hwang, Jonathan S. Dufour, Gregory G. Knapik, Thomas Best, Safdar N. Khan, Ehud Mendel, William S. Marras

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Background Accurate geometry of the trunk musculature is essential for reliably estimating spinal loads in biomechanical models. Currently, many models employ straight muscle path assumptions that yield far less accurate tissue loads, particularly in extreme postures. Precise muscle moment-arms and physiological cross-sectional areas are important when incorporating curved muscle geometry in biomechanical models. The objective of this study was to develop a predictive model of moment arms and physiological cross-sectional areas of trunk musculature at multiple levels in the thoracic/lumbar spine as a function of anthropometric measures. Methods Based on magnetic resonance imaging data from thirty subjects (10 male and 20 female) reported in a previous study, a polynomial regression analysis was conducted to estimate the muscle moment-arms and physiological cross-sectional areas of trunk muscles through thoracic/lumbar spine as a function of vertebral level, gender, age, height, and body mass. Findings Gender, body mass, and height were the best predictors of muscle moment-arms and physiological cross-sectional areas. The predictability of muscle parameters tended to be higher for erector spinae than other muscles. Most muscles showed a curved muscle path along the thoracic/lumbar spine. Interpretation The polynomial regression model of the muscle geometry in this study generally showed good predictability compared to previous reports. The predictive model in this study will be useful to develop personalized biomechanical models that incorporate curved trunk muscle geometries.

Original languageEnglish (US)
Pages (from-to)60-64
Number of pages5
JournalClinical Biomechanics
Volume37
DOIs
StatePublished - Aug 1 2016
Externally publishedYes

Fingerprint

Spine
Magnetic Resonance Imaging
Muscles
Body Height
Thorax
Statistical Models
Posture
Regression Analysis

Keywords

  • Biomechanical model
  • Cross-sectional area
  • Moment-arm
  • Muscle geometry
  • Regression model
  • Spine

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine

Cite this

Prediction of magnetic resonance imaging-derived trunk muscle geometry with application to spine biomechanical modeling. / Hwang, Jaejin; Dufour, Jonathan S.; Knapik, Gregory G.; Best, Thomas; Khan, Safdar N.; Mendel, Ehud; Marras, William S.

In: Clinical Biomechanics, Vol. 37, 01.08.2016, p. 60-64.

Research output: Contribution to journalArticle

Hwang, Jaejin ; Dufour, Jonathan S. ; Knapik, Gregory G. ; Best, Thomas ; Khan, Safdar N. ; Mendel, Ehud ; Marras, William S. / Prediction of magnetic resonance imaging-derived trunk muscle geometry with application to spine biomechanical modeling. In: Clinical Biomechanics. 2016 ; Vol. 37. pp. 60-64.
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