Automated Finite Element Meshing of the Lumbar Spine: Verification and Validation with 18 Specimen-specific Models
Daniel Felix Ritchie School of Engineering and Computer Science, Center for Orthopaedic Biomechanics, Mechanical and Materials Engineering
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 7.8 Nm with follower loads up to 1000 N. 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.
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Campbell, J.Q, et al. “Automated Finite Element Meshing of the Lumbar Spine: Verification and Validation with 18 Specimen-Specific Models.” Journal of Biomechanics, vol. 49, no. 13, 2016, pp. 2669–2676. doi: 10.1016/j.jbiomech.2016.05.025.