New treatments for bone diseases require testing in animal models before clinical translation, and the mouse tibia is among the most common models. 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 experimental data. Different modelling techniques can be used to estimate the bone properties, and the accuracy associated with each is unclear. The aim of this study was to evaluate the ability of different microCT-based microFE models to predict the mechanical properties of the mouse tibia under compressive load. Twenty tibiae were microCT scanned at 10.4 µm voxel size and subsequently compressed at 0.03 mm/s until failure. Stiffness and failure load were measured from the load–displacement curves. Different microFE models were generated from each microCT image, with hexahedral or tetrahedral mesh, and homogeneous or heterogeneous material properties. Prediction accuracy was comparable among models. The best correlations between experimental and predicted mechanical properties, as well as lower errors, were obtained for hexahedral models with homogeneous material properties. Experimental stiffness and predicted stiffness were reasonably well correlated (R² = 0.53–0.65, average error of 13–17%). A lower correlation was found for failure load (R² = 0.21–0.48, average error of 9–15%). Experimental and predicted mechanical properties normalized by the total bone mass were strongly correlated (R² = 0.75–0.80 for stiffness, R² = 0.55–0.81 for failure load). In conclusion, hexahedral models with homogeneous material properties based on in vivo microCT images were shown to best predict the mechanical properties of the mouse tibia.

骨疾病的新疗法需要在临床转化之前在动物模型中进行测试，而小鼠胫骨是最常见的模型之一。在对实验数据进行适当验证后，基于体内微计算机断层扫描 (microCT) 的微有限元 (microFE) 模型可用于非侵入性地预测骨强度。可以使用不同的建模技术来估计骨骼特性，但每种技术的准确性尚不清楚。本研究的目的是评估不同基于 microCT 的 microFE 模型预测压缩负载下小鼠胫骨机械性能的能力。以 10.4 µm 体素大小对 20 根胫骨进行 microCT 扫描，随后以 0.03 mm/s 的速度压缩直至失效。刚度和失效载荷是根据载荷-位移曲线测量的。根据每个 microCT 图像生成不同的 microFE 模型，具有六面体或四面体网格以及均质或异质材料属性。模型之间的预测精度相当。对于具有均匀材料特性的六面体模型，获得了实验和预测机械特性之间的最佳相关性以及较低的误差。实验刚度和预测刚度具有相当好的相关性（ R ² = 0.53–0.65，平均误差为 13–17%）。失效载荷的相关性较低（ R ² = 0.21–0.48，平均误差为 9–15%）。通过总骨量归一化的实验和预测机械性能密切相关（对于刚度， R ² = 0.75–0.80，对于失效载荷， R ² = 0.55–0.81）。 总之，基于体内 microCT 图像的具有均质材料特性的六面体模型被证明能够最好地预测小鼠胫骨的机械特性。