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Accuracy of Individual Trabecula Segmentation Based Plate and Rod Finite Element Models in Idealized Trabecular Bone Microstructure

机译:理想小梁骨微结构中基于小梁分割的板和杆有限元模型的准确性

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摘要

Currently, specimen-specific micro finite element (μFE) analysis based micro computed tomography (μCT) images have become a major computational tool for the assessment of the mechanical properties of human trabecular bone. Despite the fine characterization of the three-dimensional (3D) trabecular microstructure based on high-resolution μCT images, conventional μFE models with each voxel converted to an element are not efficient in predicting the nonlinear failure behavior of bone due to a prohibitive computational cost. Recently, a highly efficient individual trabecula segmentation (ITS)-based plate and rod (PR) modeling technique has been developed by substituting individual plates and rods with shell and beam elements, respectively. In this technical brief, the accuracy of novel PR μFE models was examined in idealized microstructure models over a broad range of trabecular thicknesses. The Young's modulus and yield strength predicted by simplified PR models strongly correlated with those of voxel models at various voxel sizes. The conversion from voxel models to PR models resulted in an ∼762-fold reduction in the largest model size and significantly accelerated the nonlinear FE analysis. The excellent predictive power of the PR μFE models, demonstrated in an idealized trabecular microstructure, provided a quantitative mechanical basis for this promising tool for an accurate and efficient assessment of trabecular bone mechanics and fracture risk.
机译:当前,基于标本的微有限元(μFE)分析的微型计算机断层扫描(μCT)图像已成为评估人小梁骨力学性能的主要计算工具。尽管基于高分辨率μCT图像对三维(3D)小梁微结构进行了精细表征,但由于计算成本过高,将每个体素转换为元素的常规μFE模型无法有效地预测骨骼的非线性破坏行为。最近,通过分别用壳单元和梁单元替换单个板和杆,已经开发了一种高效的基于单个小梁分割(ITS)的板和杆(PR)建模技术。在本技术简介中,在各种小梁厚度范围内的理想微结构模型中检验了新型PRμFE模型的准确性。简化的PR模型预测的杨氏模量和屈服强度与各种体素大小下的体素模型的杨氏模量和屈服强度密切相关。从体素模型到PR模型的转换导致最大模型尺寸减少了762倍,并大大加速了非线性有限元分析。 PRμFE模型的出色预测能力在理想的小梁微结构中得到了证明,为该有前途的工具提供了定量的机械基础,从而可以准确有效地评估小梁的骨力学和骨折风险。

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