Incorporating uncertainty in mechanical properties for finite element-based evaluation of bone mechanics
Incorporating uncertainty in mechanical properties for finite element-based evaluation of bone mechanics
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DOI：
10.1016/j.jbiomech.2007.03.013
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发表时间：
2007-01-01
影响因子：
2.4
通讯作者：
Rullkoetter, Paul J.
中科院分区：
文献类型：
--
作者：
Laz, Peter J.;Stowe, Joshua Q.;Rullkoetter, Paul J.
根据计算机断层扫描(CT)数据开发的骨的有限元(FE)模型用于评估应力和应变、种植体的载荷传递和固定以及骨折的可能性。用于将Hounsfield单位的CT扫描数据转换为模数和强度的实验推导的关系包含大量的散布。这些关系的分散有可能影响骨骼研究的结果和结论。本研究的目的是开发一个计算效率高的基于概率有限元的平台，该平台能够包含骨性能关系中的不确定性，并将该模型应用于代表性分析；在站立载荷条件下，预测股骨近端的应力和骨折风险的变异性。根据已发表的股骨近端强度和弹性系数关系的可变性，概率分析预测了应力和风险的分布。对于被分析的五个股骨，I和99百分位数的界限对于应力的平均变化为17.3兆帕，对于风险的平均值为0.28。在每根股骨中，预测的风险变异性大于计算的平均风险的50%，这对临床评估具有明显的意义。结果使用改进的平均值(AMV)方法只需要7次分析试验(I H)，与1000次蒙特卡罗模拟(400 H)相比，差异不到2%。开发的概率建模平台对骨骼研究具有广泛的适用性，并可以类似地实现以调查其他载荷条件、结构、不确定性源或感兴趣的输出测量。(C)2007爱思唯尔有限公司。保留所有权利。
Finite element (FE) models of bone, developed from computed tomography (CT) scan data, are used to evaluate stresses and strains, load transfer and fixation of implants, and potential for fracture. The experimentally derived relationships used to transform CT scan data in Hounsfield unit to modulus and strength contain substantial scatter. The scatter in these relationships has potential to impact the results and conclusions of bone studies. The objectives of this study were to develop a computationally efficient probabilistic FE-based platform capable of incorporating uncertainty in bone property relationships, and to apply the model to a representative analysis; variability in stresses and fracture risk was predicted in five proximal femurs under stance loading conditions. Based on published variability in strength and modulus relationships derived in the proximal femur, the probabilistic analysis predicted the distributions of stress and risk. For the five femurs analyzed, the I and 99 percentile bounds varied by an average of 17.3 MPa for stress and by 0.28 for risk. In each femur, the predicted variability in risk was greater than 50% of the mean risk calculated, with obvious implications for clinical assessment. Results using the advanced mean value (AMV) method required only seven analysis trials (I h) and differed by less than 2% when compared to a 1000-trial Monte-Carlo simulation (400h). The probabilistic modeling platform developed has broad applicability to bone studies and can be similarly implemented to investigate other loading conditions, structures, sources of uncertainty, or output measures of interest. (c) 2007 Elsevier Ltd. All rights reserved.