Cartilage Thickness Distribution Affects Computational Model Predictions of Cervical Spine Facet Contact Parameters

With motion-sparing disk replacement implants gaining popularity as an alternative to anterior cervical discectomy and fusion (ACDF) for the treatment of certain spinal degenerative disorders, recent laboratory investigations have studied the effects of disk replacement and implant design on spinal kinematics and kinetics. Particularly relevant to cervical disk replacement implant design are any postoperative changes in solid stresses or contact conditions in the articular cartilage of the posterior facets, which are hypothesized to lead to adjacent-level degeneration. Such changes are commonly investigated using finite element methods, but significant simplification of the articular geometry is generally employed. The impact of such geometric representations has not been thoroughly investigated. In order to assess the effects of different models of cartilage geometry on load transfer and contact pressures in the lower cervical spine, a finite element model was generated using cadaver-based computed tomography imagery. Mesh resolution was varied in order to establish model convergence, and cadaveric testing was undertaken to validate model predictions. The validated model was altered to include four different geometric representations of the articular cartilage. Model predictions indicate that the two most common representations of articular cartilage geometry result in significant reductions in the predictive accuracy of the models. The two anatomically based geometric models exhibited less computational artifact, and relatively minor differences between them indicate that contact condition predictions of spatially varying thickness models are robust to anatomic variations in cartilage thickness and articular curvature. The results of this work indicate that finite element modeling efforts in the lower cervical spine should include anatomically based and spatially varying articular cartilage thickness models. Failure to do so may result in loss of fidelity of model predictions relevant to investigations of physiological import.