Facta Universitatis. Series: Mechanical Engineering (Dec 2014)

A FINITE ELEMENT MODEL OF IN VIVO MOUSE TIBIAL COMPRESSION LOADING: INFLUENCE OF BOUNDARY CONDITIONS

  • Hajar Razi,
  • Annette I Birkhold,
  • Manfred Zehn,
  • Georg N Duda,
  • Bettina M Willie,
  • Sara Checa

Journal volume & issue
Vol. 12, no. 3
pp. 195 – 207

Abstract

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Though bone is known to adapt to its mechanical challenges, the relationship between the local mechanical stimuli and the adaptive tissue response seems so far unclear. A major challenge appears to be a proper characterization of the local mechanical stimuli of the bones (e.g. strains). The finite element modeling is a powerful tool to characterize these mechanical stimuli not only on the bone surface but across the tissue. However, generating a predictive finite element model of biological tissue strains (e.g., physiological-like loading) encounters aspects that are inevitably unclear or vague and thus might significantly influence the predicted findings. We aimed at investigating the influence of variations in bone alignment, joint contact surfaces and displacement constraints on the predicted strains in an in vivo mouse tibial compression experiment. We found that the general strain state within the mouse tibia under compressive loading was not affected by these uncertain factors. However, strain magnitudes at various tibial regions were highly influenced by specific modeling assumptions. The displacement constraints to control the joint contact sites appeared to be the most influential factor on the predicted strains in the mouse tibia. Strains could vary up to 150% by modifying the displacement constraints. To a lesser degree, bone misalignment (from 0 to 20°) also resulted in a change of strain (+300 µε = 40%). The definition of joint contact surfaces could lead to up to 6% variation. Our findings demonstrate the relevance of the specific boundary conditions in the in vivo mouse tibia loading experiment for the prediction of local mechanical strain values using finite element modeling.