Abstract
Finite element analysis (FEA) provides the current reference standard for numerical simulation 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 population-averaged method is based on average cartilage thickness maps and therefore allows for a more accurate and individualized cartilage geometry estimation when combined with SSM. 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 based on the estimation of acetabular and femoral head radius. Still, contact stress was overestimated and contact area was underestimated for all cartilage anatomy prediction methods. Linear and non-linear DEA methods differed mainly in peak stress results with the non-linear 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 accurate predictions while offering an efficient platform to conduct population-wide analyses of hip contact mechanics.
Highlights
Hip osteoarthritis (OA) is a disabling condition with a lifetime risk of 25% (Murphy et al, 2010)
The general workflow for this study was: (1) to develop the discrete element models with both linear and non-linear definitions and benchmark them against a validated finite element analysis (FEA) model, (2) assign cartilage geometry using a novel methodology based on population-averaged thickness maps and compare it with popular cartilage geometry prediction methods, and (3) evaluate the accuracy of cartilage geometry prediction methods and their impact on contact mechanics using Discrete element analysis (DEA)
We demonstrated that the novel populationaveraged prediction method to estimate cartilage geometry for DEA models yielded cartilage contact mechanics that were in better agreement with subject-specific FEA models when compared to the spherical fit or constant thickness methods
Summary
Hip osteoarthritis (OA) is a disabling condition with a lifetime risk of 25% (Murphy et al, 2010). Most cases of hip OA are theorized to be the consequence of unfavorable mechanical conditions (Reijman et al, 2005; Ganz et al, 2008). Structural hip deformities are believed to cause deleterious stresses and strains in the cartilage, resulting in mechanical damage and hip OA (Genda et al, 2001; Mavcic et al, 2002). There is a high prevalence of structural hip deformities amongst the asymptomatic population that show no radiographic evidence of joint space narrowing indicative of OA (Anderson et al, 2016). Computational techniques that afford prediction of cartilage stress in appropriately-powered studies would improve understanding of the pathogenesis of hip OA. The development and analysis of these computer models is time-consuming and technically-challenging due to laborious pre-processing and the need for specific domain expertise, which may explain why most modeling studies of the hip have utilized small sample sizes (Henak et al, 2013)
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