Optimization of the failure criterion in micro-Finite Element models of the mouse tibia for the non-invasive prediction of its failure load in preclinical applications.

New treatments against osteoporosis require testing in animal models and the mouse tibia is among the most common studied anatomical sites. In vivo micro-Computed Tomography (microCT) based micro-Finite Element (microFE) models can be used for predicting the bone strength non-invasively, after proper validation against experiments. The aim of this study was to evaluate the ability of different microCT-based bone parameters and microFE models to predict tibial structural mechanical properties in compression. Twenty tibiae were scanned at 10.4 μm voxel size and subsequently tested in uniaxial compression at 0.03 mm/s until failure. Stiffness and failure load were measured from the load-displacement curves. Standard morphometric parameters were measured from the microCT images. The spatial distribution of bone mineral content (BMC) was evaluated by dividing the tibia into 40 regions. MicroFE models were generated by converting each microCT image into a voxel-based mesh with homogeneous isotropic material properties. Failure load was estimated by using different failure criteria, and the optimized parameters were selected by minimising the errors with respect to experimental measurements. Experimental and predicted stiffness were moderately correlated (R2 = 0.65, error = 14% ± 8%). Normalized failure load was best predicted by microFE models (R2 = 0.81, error = 9% ± 6%). Failure load was not correlated to the morphometric parameters and weakly correlated with some geometrical parameters (R2 < 0.37). In conclusion, microFE models can improve the current estimation of the mouse tibia structural properties and in this study an optimal failure criterion has been defined. Since it is a non-invasive method, this approach can be applied longitudinally for evaluating temporal changes in the bone strength.