Uncertainty quantification for personalized analyses of human proximal femurs

JOURNAL OF BIOMECHANICS

卷:49

Article

Wille, Hagen¹  Ruess, Martin²  Rank, Ernst^1,4  Yosibash, Zohar³ 

[1] Tech Univ Munich, Chair Computat Engn, D-80290 Munich, Germany

[2] Delft Univ Technol, Fac Aerosp Engn, Delft, Netherlands

[3] Ben Gurion Univ Negev, Dept Mech Engn, IL-84105 Beer Sheva, Israel

[4] Tech Univ Munich, Inst Adv Study, D-80290 Munich, Germany

关键词: Femur;    Personalized medicine;    Uncertainty quantification;    Polynomial chaos;    Finite cell method;   

DOI  :  10.1016/j.jbiomech.2015.11.013

来源: Elsevier

【 摘 要 】

Computational models for the personalized analysis of human femurs contain uncertainties in bone material properties and loads, which affect the simulation results. To quantify the influence we developed a probabilistic framework based on polynomial chaos (PC) that propagates stochastic input variables through any computational model. We considered a stochastic E-rho relationship and a stochastic hip contact force, representing realistic variability of experimental data. Their influence on the prediction of principal strains (epsilon(1) and epsilon(3)) was quantified for one human proximal femur, including sensitivity and reliability analysis. Large variabilities in the principal strain predictions were found in the cortical shell of the femoral neck, with coefficients of variation of 40%. Between 60 and 80% of the variance in epsilon(1) and e3 are attributable to the uncertainty in the E-rho relationship, while approximate to 10% are caused by the load magnitude and 5-30% by the load direction. Principal strain directions were unaffected by material and loading uncertainties. The antero-superior and medial inferior sides of the neck exhibited the largest probabilities for tensile and compression failure, however all were very small (p(f) < 0.001). In summary, uncertainty quantification with PC has been demonstrated to efficiently and accurately describe the influence of very different stochastic inputs, which increases the credibility and explanatory power of personalized analyses of human proximal femurs. (C) 2015 Elsevier Ltd. All rights reserved.

【 授权许可】

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