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Proximal femur anatomy and bone mineral density vary widely among individuals, precluding the use of one predefined finite element (FE) model to determine the stress field for all proximal femurs. This variability poses a challenge in current prosthetic hip design approach. Given the numerous options for generating computed tomography (CT)‐based FE models, selecting the best methods for defining the mechanical behavior of the proximal femur is difficult. In this study, a combination of computational and experimental approaches was used to explore the susceptibility of the predicted stress field of the proximal femur to different combinations of density–elasticity relationships, element type, element size, and calibration error. Our results suggest that FE models with first‐order voxelized elements generated by the Keyak and Falkinstein density–elasticity relationship or quadratic tetrahedral elements generated by the Morgan density–elasticity relationship lead to accurate estimations of the mechanical behavior of human femurs. Other combinations of element size, element type, and mathematical relationships produce less accurate results, especially in the cortical bone of the femoral neck and calcar region. The voxelized model was more susceptible to variation of element size and density–elasticity relationships than FE models with quadratic tetrahedral elements. Regardless of element type, the stress fields predicted by the Keyak and Falkinstein and the Morgan relationships were the most robust to calibration error when deriving material density from CT‐generated Hounsfield data. These results provide insight into the implementation of a robust platform for designing patient‐specific implants capable of maintaining or modifying the stress in bones. [ABSTRACT FROM AUTHOR]

In total hip arthroplasty and particularly in revision surgery, computer assisted pre-operative prediction of the best possible anchorage strategy for implant fixation would be a great help to the surgeon. Computer simulation relies on validated numerical models. In the current study, three density-elasticity relationships (No. 1-3) from the literature for inhomogeneous material parameter assignment from CT data in automated finite element (FE) modeling of long bones were evaluated for their suitability for FE modeling of human pelvic bone. Numerical modal analysis was conducted on 10 FE models of hemipelvic bone specimens and compared to the gold standard provided by experimental modal analysis results from a previous in-vitro study on the same specimens. Overall, calculated resonance frequencies came out lower than measured values. Magnitude of mean relative deviation of numerical resonance frequencies with regard to measured values is lowest for the density-elasticity relationship No. 3 ( -15.9%) and considerably higher for both density-elasticity relationships No. 1 ( - 41.1%) and No. 2 ( -45.0%). Mean MAC values over all specimens amount to 77.8% (No. 1), 78.5% (No. 2), and 83.0% (No. 3). MAC results show, that mode shapes are only slightly influenced by material distribution. Calculated resonance frequencies are generally lower than measured values, which indicates, that numerical models lack stiffness. Even when using the best suited (No. 3) out of three investigated density-elasticity relationships, in FE modeling of pelvic bone a considerable underestimation of model stiffness has to be taken into account. [ABSTRACT FROM AUTHOR]

Abstract: Subject-specific FE-models of human long bones have to predict mechanical parameters with sufficient accuracy to be applicable in a clinical setting. One of the main aspects in subject-specific FE-models of bones regarding accuracy is the modeling of the material inhomogeneity. The goal of this study was therefore to develop FE-models of human femurs and investigate if a single validated density–elasticity relationship can be used for subject specific finite element analyses of human long bones, when the task is to predict the bone's mechanical response to load. To this aim, 23 human cadaver femurs were tested in axial compression with a load of 1000N. Strains, local displacements, and axial bone stiffness were determined. Subject-specific FE-models were developed for each bone based on quantitative CT-scans. Three different density–elasticity relationships were retrieved from the literature, and were implemented in the FE-models. The predicted mechanical values depended largely on the used equation. The most reasonable equation showed a mean error of −11% in strain prediction, a mean error of −23% in local displacement prediction, and a mean error of +23% in axial stiffness prediction. The scatter of the predictions was very low in all three categories of measurements with a 1.96 standard deviation of about 30% to the mean errors. In conclusion, a framework for subject-specific FE-models was developed that was able to predict surface strains and bone deformation with good accuracy by using a single density–elasticity relationship. However, it was also found that the most appropriate density–elasticity relationship was specimen-specific. [Copyright &y& Elsevier]

Abstract: To better understand the mechanisms underlying distal radius fracture we have developed finite element models to predict radius bone strain and fracture strength under loading conditions simulating a fall. This study compares experimental surface strains and fracture loads of the distal radius with specimen-specific finite element models to validate our model-generating algorithm. Five cadaveric forearms were instrumented with strain gage rosettes, loaded non-destructively to 300N, and subsequently loaded until failure. Finite element models were created from computed tomography data; three separate density–elasticity relationships were examined. Fracture strength was predicted for three specimens that failed at the distal radius using six different failure theories. The density–elasticity relationship providing the strongest agreement between measured and predicted strains had a correlation of r =0.90 and a root mean squared error 13% of the highest measured strain. Mean absolute percent error (11.6%) between measured and predicted fracture loads was minimized with Coulomb–Mohr failure theory and a tensile–compressive strength ratio of 0.5. These results suggest that our modeling method is a suitable candidate for the in vivo assessment of distal radius bone strain and fracture strength under fall type loading configurations. [Copyright &y& Elsevier]

Abstract: The prediction of the stress-state and fracture risk induced in bones by various loading conditions in individual patients using subject-specific finite element models still represents a challenge in orthopaedic biomechanics. The accuracy of the strain predictions reported in the literature is variable and generally not satisfactory. The aim of the present study was to evaluate if a proper choice of the density–elasticity relationship can lead to accurate strain predictions in the frame of an automatic subject-specific model generation strategy. To this aim, a combined numerical–experimental study was performed comparing finite element predicted strains with strain-gauges measurements obtained on eight cadaver proximal femurs, each instrumented with 15 rosettes mostly concentrated in the bone metaphyses, tested non-destructively in vitro under six different loading scenarios. Three different density–elasticity power relationships were selected from the literature and implemented in the finite element models derived from computed tomography data. The results of the present study confirm the great influence of the density–elasticity relationship used on the accuracy of numerical predictions. One of the tested constitutive laws provided a very good agreement (R 2=0.91, RMSE lower than 10% of the maximum measured value) between numerical calculations and experimental measurements. The presented results show, in addition, that the adoption of a single density–elasticity relationship over the whole bone density range is adequate to obtain an accuracy that is already suitable for many applications. [Copyright &y& Elsevier]

Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex. Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy. We therefore: (i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models. (ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship. (iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment. Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models. In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength. • New femur FE models including Cortical Bone Mapping (CBM), i.e. local estimates of cortical thickness&density from CT data • CBM models improved bone strain estimates in the elastic range • Thus, CBM models can be used to estimate bone strains in exercise • Cortical-specific (experimental or optimised) density-elasticity law could not improve accuracy of CBM models • CBM models do not accurately estimate strength yet, but provide accurate strain data to develop biofidelic failure criteria [ABSTRACT FROM AUTHOR]