Accounting for spatial variation of trabecular anisotropy with subject-specific finite element modeling moderately improves predictions of local subchondral bone stiffness at the proximal tibia. (5th July 2017)

• Record Type: Journal Article
• Authors: Nazemi, S. Majid; Kalajahi, S. Mehrdad Hosseini; Cooper, David M.L.; Kontulainen, Saija A.; Holdsworth, David W.; Masri, Bassam A.; Wilson, David R.; Johnston, James D.
• Is Part Of: Journal of biomechanics. Volume 59(2017)
• Page Start: 101
• Page End: 108
• Publication Date: 2017-07-05
• Subjects: Finite element modeling -- Anisotropy -- Quantitative Computed Tomography -- Proximal tibia -- Subchondral bone stiffness; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2017.05.018
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Introduction: Previously, a finite element (FE) model of the proximal tibia was developed and validated against experimentally measured local subchondral stiffness. This model indicated modest predictions of stiffness ( R 2 = 0.77, normalized root mean squared error (RMSE%) = 16.6%). Trabecular bone though was modeled with isotropic material properties despite its orthotropic anisotropy. The objective of this study was to identify the anisotropic FE modeling approach which best predicted (with largest explained variance and least amount of error) local subchondral bone stiffness at the proximal tibia. Methods: Local stiffness was measured at the subchondral surface of 13 medial/lateral tibial compartments using in situ macro indentation testing. An FE model of each specimen was generated assuming uniform anisotropy with 14 different combinations of cortical- and tibial-specific density-modulus relationships taken from the literature. Two FE models of each specimen were also generated which accounted for the spatial variation of trabecular bone anisotropy directly from clinical CT images using grey-level structure tensor and Cowin's fabric-elasticity equations. Stiffness was calculated using FE and compared to measured stiffness in terms of R 2 and RMSE%. Results: The uniform anisotropic FE model explained 53–74% of the measured stiffness variance, with RMSE% ranging from 12.4 to 245.3%. The models which accounted for spatial variation of trabecular bone anisotropy predicted 76–79% of the variance in stiffness with RMSE% being 11.2–11.5%. Conclusions: Of the 16 evaluated finite element models in this study, the combination of Synder and Schneider (for cortical bone) and Cowin's fabric-elasticity equations (for trabecular bone) best predicted local subchondral bone stiffness.