Incorporating Ligament Laxity in the Upper Cervical Spine for Finite Element Modeling

Abstract

Background Context: Predicting physiological range of motion (ROM) using a finite element (FE) model of the upper cervical spine requires the incorporation of ligament laxity. The effect of ligament laxity can only be observed on a macro level of joint motion and is lost once ligaments have been dissected and preconditioned for experimental testing. As a result, ligament laxity values are not directly available in the literature for use in FE models. Purpose: The purpose of the current study it to propose an optimization process that can be used to determine a set of ligament laxity values for upper cervical spine FE models. Furthermore, an FE model that includes ligament laxity will be analyzed against experimental data for physiological ROM as well as experimental data for the increase in ROM when a type II odontoid fracture is introduced. Study Design/Setting: The upper cervical spine FE model was adapted from a fiftieth percentile male full body model developed with the Global Human Body Models Consortium (GHMBC). FE modeling was performed in LS-DYNA (Livermore Software Technology Group, Livermore CA) and LS-OPT (Livermore Software Technology Group, Livermore CA) was used for ligament laxity optimization. Methods: Ordinate-based curve matching was used to minimize the mean squared error (MSE) between computed load-rotation curves and experimental load-rotation curves under flexion, extension and axial rotation with pure moment loads from 0-3.5Nm. Lateral bending was excluded from the optimization since the upper cervical spine is primarily responsible for flexion, extension and axial rotation. Based on recommendations from the literature, four varying inputs representing laxity in select ligaments were optimized in order to minimize the MSE. Funding was provided by the Natural Sciences and Engineering Research Council of Canada as well as GHMBC. There are no conflicts of interest to be reported. Results: The MSE was reduced to 0.28 in the FE model with optimized ligament laxity compared to an MSE 0f 4.16 in the FE model without laxity. In all load cases, incorporating ligament laxity improved the agreement between the ROM of the FE model and the ROM of the experimental data. The ROM for axial rotation and extension was within one standard deviation of the0 experimental data. The ROM for flexion and lateral bending was outside one standard deviation of the experimental data but a compromise was required in order to use one set of ligament laxity values to achieve a best fit to all load cases. After a type II odontoid fracture was incorporated into the model, the increase in ROM was in good agreement with experimental data from the literature. Conclusions: The optimization approach used in this study provided values for ligament laxities that, when incorporated into the FE model, improved the ROM response when compared to experimental data. Successfully modeling a type II odontoid fracture showcased the robustness of the FE model which can now be used in future biomechanics studies.