Indentation-Based Material Parameter Identifiability in Anisotropic Soft Tissues

Accurate quantification of soft tissue material parameters is essential for tissue mechanics simulations, medical device design, surgical planning, and non-invasive diagnostics. Finite element analysis (FEA) is commonly employed, but generating accurate simulations often requires patient- and location-specific tissue material parameters. Although soft tissue constitutive models are well-developed, practical implementation is limited by the invasive nature of experiments required for fitting model parameters. Non-invasive methods, such as indentation and suction, offer in vivo applicability but typically lack analytical solutions that would allow direct fitting of material parameters. Consequently, parameter identification becomes an inverse problem solved via FEA, which is often ill-posed, yielding multiple sets of seemingly optimal parameters, especially with limited experimental data. This non-uniqueness undermines the reliable prediction of tissue response under varying loads. This study investigates the identifiability of transversely isotropic hyperelastic material parameters through macro-scale indentation, combining simultaneous measurements of force and full-field surface deformation. We use a simplified two-parameter constitutive model to represent a soft composite phantom and compare the homogenized parameters identified through indentation with those obtained from separate analyses of the matrix and fiber materials. Our findings indicate that a measurement error of 5% leads to certainty bounds of ±5.2% and ±28% for the isotropic and anisotropic parameters, respectively, when utilizing combined force-deformation data. In contrast, when only force data is considered, they are ±22.5% and ±210%, respectively. These results demonstrate that surface deformation measurements are crucial for uniquely identifying anisotropic hyperelastic parameters through indentation. Further research is needed to evaluate identifiability in more complex models and in vivo indentation scenarios.