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The purpose of this study was to seek broad verification and validation of human lumbar spine finite element models created using a previously published automated algorithm. The automated algorithm takes segmented CT scans of lumbar vertebrae, automatically identifies important landmarks and contact surfaces, and creates a finite element model. Mesh convergence was evaluated by examining changes in key output variables in response to mesh density. Semi-direct validation was performed by comparing experimental results for a single specimen to the automated finite element model results for that specimen with calibrated material properties from a prior study. Indirect validation was based on a comparison of results from automated finite element models of 18 individual specimens, all using one set of generalized material properties, to a range of data from the literature. A total of 216 simulations were run and compared to 186 experimental data ranges in all six primary bending modes up to 7.8Nm with follower loads up to 1000N. Mesh convergence results showed less than a 5% difference in key variables when the original mesh density was doubled. The semi-direct validation results showed that the automated method produced results comparable to manual finite element modeling methods. The indirect validation results showed a wide range of outcomes due to variations in the geometry alone. The studies showed that the automated models can be used to reliably evaluate lumbar spine biomechanics, specifically within our intended context of use: in pure bending modes, under relatively low non-injurious simulated in vivo loads, to predict torque rotation response, disc pressures, and facet forces.

Understanding the kinematics of the lumbosacral spine and the individual functional spinal units (FSU) is essential in assessing spine mechanics and implant performance. The lumbosacral spine and the FSU are comprised of bones and complex soft tissues such as intervertebral discs (IVD) and ligaments. Prior studies have focused on the behavior of isolated structures, but the contribution of each structure to the overall kinematics of the spine needs to be further understood. In this study, the behavior of various structural conditions was determined by experimentally dissecting each ligament in a stepwise fasion until only the IVD remained, and applying loading conditions to the FSU. The FE model was validated through optimization to match the in vitro load-deflection characteristics and contact mechanics for the various structural configurations.

Finite element (FE) models of the spine have been used to assess natural and pathological spine mechanics and evaluate performance of various fusion and posterior stabilization devices [1–3]; however, analysis times may be prohibitive for clinical and design phase assessments. Muscle-actuated, rigid body models have also been developed and used to estimate spinal loading conditions during simulated activities [4]. Although rigid body dynamics platforms typically require less computational time, they are unable to evaluate internal stresses and strains in deformable structures. This study proposes to develop a combined rigid – deformable surrogate spine model where the behavior of the intervertebral disc, facet cartilage and ligaments are replicated by simulated mechanical constraint at desired levels. The explicit FE platform is able to accommodate the spectrum of representations, including fully deformable, fully rigid body, implanted, or any combination. Accordingly, the objective of the current study was to assess the ability of a combined rigid-deformable spine model to accurately reproduce the behavior of the fully deformable representation in the natural state with improved computational efficiency. Specifically, this study compared results for a lumbar (L1-L5) spine under follower load and moment conditions for representations ranging from fully deformable to fully rigid. The combined rigid-deformable model includes the deformable disc, facet cartilage contact, ligament representations at L4-L5, while the other levels are modeled using a simplified mechanical constraint.