Abstract
Constitutive modeling of the meniscus is critical in areas like knee surgery and tissue engineering. At low strain rates, the meniscus can be described using a hyperelastic model. Calibration of hyperelastic material models of the meniscus is challenging on many fronts due to material variability and friction. In this study, we present a framework to determine the hyperelastic material parameters of porcine meniscus (and similar soft tissues) using no-slip uniaxial compression experiments. Because of the nonhomogeneous deformation in the specimens, a finite element solution is required at each step of the iterative calibration process. We employ a Bayesian calibration approach to account for the inherent material variability and a Bayesian optimization approach to minimize the resulting cost function in the material parameter space. Cylindrical specimens of porcine meniscus from the anterior, middle and posterior regions are tested up to 30% compressive strain and the Yeoh form of hyperelastic strain energy density function is used to describe the material response. The results show that the Yeoh form is able to accurately describe the compressive response of porcine meniscus and that the Bayesian calibration and optimization approaches are able to calibrate the model in a computationally efficient manner while taking into account the inherent material variability. The results also show that the shear modulus or the initial stiffness is roughly uniform across the different areas of the meniscus, but there is significant spatial heterogeneity in the response at high strains. In particular, the middle region is considerably stiffer at high strains. This heterogeneity is important to consider in modeling the response of the meniscus for clinical applications.
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Long, T., Shende, S., Lin, CY. et al. Experiments and hyperelastic modeling of porcine meniscus show heterogeneity at high strains. Biomech Model Mechanobiol 21, 1641–1658 (2022). https://doi.org/10.1007/s10237-022-01611-3
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DOI: https://doi.org/10.1007/s10237-022-01611-3