Performance of QCT-Derived scapula finite element models in predicting local displacements using digital volume correlation

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Abstract

Subject-specific finite element models (FEMs)of the shoulder complex are commonly used to predict differences in internal load distribution due to injury, treatment or disease. However, these models rely on various underlying assumptions, and although experimental validation is warranted, it is difficult to obtain and often not performed. The goal of the current study was to quantify the accuracy of local displacements predicted by subject-specific QCT-based FEMs of the scapula, compared to experimental measurements obtained by combining digital volume correlation (DVC)and mechanical loading of cadaveric specimens within a microCT scanner. Four cadaveric specimens were loaded within a microCT scanner using a custom-designed six degree-of-freedom hexapod robot augmented with carbon fiber struts for radiolucency. BoneDVC software was used to quantify full-field experimental displacements between pre- and post-loaded scans. Corresponding scapula QCT-FEMs were generated and three types of boundary conditions (BC)(idealized-displacement, idealized-force, and DVC-derived)were simulated for each specimen. DVC-derived BCs resulted in the closest match to the experimental results for all specimens (best agreement: slope ranging from 0.87 to 1.09; highest correlation: r2 ranging from 0.79 to 1.00). In addition, a two orders of magnitude decrease was observed in root-mean-square error when using QCT-FEMs with simulated DVC-derived BCs compared to idealized-displacement and idealized-force BCs. The results of this study demonstrate that scapula QCT-FEMs can accurately predict local experimental full-field displacements if the BCs are derived from DVC measurements.

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Kusins, J., Knowles, N., Ryan, M., Dall’Ara, E., & Ferreira, L. (2019). Performance of QCT-Derived scapula finite element models in predicting local displacements using digital volume correlation. Journal of the Mechanical Behavior of Biomedical Materials, 97, 339–345. https://doi.org/10.1016/j.jmbbm.2019.05.021

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