Abstract
BackgroundExperimental validation is the gold standard for the development of FE predictive models of bone. Employing multiple loading directions could improve this process. To capture the correct directional response of a sample, the effect of all influential parameters should be systematically considered. This study aims to determine the impact of common experimental parameters on the proximal femur’s apparent stiffness.MethodsTo that end, a parametric approach was taken to study the effects of: repetition, pre-loading, re-adjustment, re-fixation, storage, and μCT scanning as random sources of uncertainties, and loading direction as the controlled source of variation in both stand and side-fall configurations. Ten fresh-frozen proximal femoral specimens were prepared and tested with a novel setup in three consecutive sets of experiments. The neutral state and 15-degree abduction and adduction angles in both stance and fall configurations were tested for all samples and parameters. The apparent stiffness of the samples was measured using load-displacement data from the testing machine and validated against marker displacement data tracked by DIC cameras.ResultsAmong the sources of uncertainties, only the storage cycle affected the proximal femoral apparent stiffness significantly. The random effects of setup manipulation and intermittent μCT scanning were negligible. The 15∘ deviation in loading direction had a significant effect comparable in size to that of switching the loading configuration from neutral stance to neutral side-fall.ConclusionAccording to these results, comparisons between the stiffness of the samples under various loading scenarios can be made if there are no storage intervals between the different load cases on the same samples. These outcomes could be used as guidance in defining a highly repeatable and multi-directional experimental validation study protocol.
Highlights
Validation of numerical predictive and monitoring models is carried out against experimental results
Many biomechanical experiments done on femur are based on single tests per sample [12,13,14,15,16,17,18]
finite element (FE) models validated against such experimental data are at risk of being biased towards those specific experimental load cases
Summary
Validation of numerical predictive and monitoring models is carried out against experimental results. This validation process determines how accurate a model can mimic reality [1, 2]. Image-based methods, such as CT-based finite element (FE) models, have become state-of-the-art in biomechanical bone research, with clinical use cases [3,4,5,6,7]. Using these models, the risk of fracture in patients with underlying conditions, such as osteoporosis, can be non-invasively estimated to guide treatment efforts and lessen the consequent immobilization burden. This study aims to determine the impact of common experimental parameters on the proximal femur’s apparent stiffness
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