Estimation of spinal loads using a detailed finite element model of the L4-L5 lumbar segment derived by medical imaging kinematics; a feasibility study

Document Type: Conference Proceedings

Authors

1 M.Sc. Student, School of Mechanical Engineering, College 2 of Engineering Schools, University of Tehran. Tehran, Iran.

2 Ph.D., School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.

Abstract

Introduction:
Low back pain is the most prevalent orthopedic disorder and the first main cause of poor working functionality in developed as wells as many developing countries. In Absence of noninvasive in vivo measurement approaches, biomechanical models are used to estimate mechanical loads on human joints during physical activities. To estimate joint loads via musculoskeletal models, the calculation of muscle forces is of importance. It is however difficult to estimate muscle forces as the number of muscles, i.e. unknown parameters, is far more than the existing degrees of freedom; the system is highly redundant.
 
Materials and Methods:
In this study, instead of muscle forces estimation, their effects (i.e., rotations and displacements) is measured by medical imaging techniques and prescribed to a detailed finite element model of the L4-L5 spine segment to determine intervertebral disc pressure as a representative of compressive forces acting on the joint. A previously validated geometrically-detailed passive finite element model of the L4-L5 segment was used. Disc, facet joints, vertebrae, and ligaments were simulated with appropriate elements/material properties. Rotations and displacements of the L4 and L5 vertebrae from supine to upright and from upright to trunk flexion of 10 degrees were measured via x-ray imaging.
 
Results:
The kinematics were prescribed to the L4 and L5 centroids. Maximal intradiscal pressure of
~0.45 MPa was predicted for the simulated tasks that was in agreement with in vivo data in the literature.
 
Conclusion:
Preliminary results indicate feasibility of this kinematics-based approach to predict in vivo spine loads.

Keywords