A Combined Geometric Morphometric and Discrete Element Modeling Approach for Hip Cartilage Contact Mechanics - Van Houcke, Jan; Audenaert, Emmanuel A.; Atkins, Penny R.; Anderson, Andrew E. (Frontiers in Bioengineering and Biotechnology, 2020)
Finite element analysis (FEA) provides the current reference standard for estimation of hip cartilage contact mechanics. Unfortunately, the development of subject-specific FEA models is a laborious process. Owed to its simplicity, Discrete Element Analysis (DEA) provides an attractive alternative to FEA. Advancements in computational morphometrics, specifically statistical shape modeling (SSM), provide the opportunity to predict cartilage anatomy without image segmentation, which could be integrated with DEA to provide an efficient platform to predict cartilage contact stresses in large populations. The objective of this study was, first, to validate linear and non-linear DEA against a previously validated FEA model and, second, to present and evaluate the applicability of a novel population-averaged cartilage geometry prediction method against previously used methods to estimate cartilage anatomy. The root mean squared error of the population-averaged cartilage geometry predicted by SSM as compared to the manually segmented cartilage geometry was 0.31 ± 0.08 mm. Identical boundary and loading conditions were applied to the DEA and FEA models. Predicted DEA stress distribution patterns and magnitude of peak stresses were in better agreement with FEA for the novel cartilage anatomy prediction method as compared to commonly used parametric methods. Still, contact stress was overestimated and contact area was underestimated for all cartilage anatomy prediction methods. Linear and nonlinear DEA methods differed mainly in peak stress results with the nonlinear definition being more sensitive to detection of high peak stresses. In conclusion, DEA in combination with the novel population-averaged cartilage anatomy prediction method provided more accurate predictions as compared to earlier methods, thus offering an efficient platform to conduct population-wide analyses of hip contact mechanics.