Determination of Soft Tissue Material Constants using Tailored Finite Element Model based Regression

Efficient and reliable tools are needed to accurately characterize the mechanical properties of soft tissue. A knowledge of Young's modulus and Poisson's ratio is essential to model mechanical behavior and quantify stresses and strain within soft tissues. The goal of this study was to investigate indirect methods for the identification of soft tissue material parameters from experimental data for irregular sample shapes and compositions. The approach was to solve an overdetermined inverse problem. Three-dimensional finite element simulations were performed, assuming a homogeneous isotropic linear elastic material. Simulation results were compared to measured surface strains obtained from Digital image correlation techniques. Material parameters were identified using an iterative approach, through the minimization of the mean square difference between simulated and measured strain data. The feasibility of this method was investigated using uniaxial tension tests of porcine vocal fold tissue samples. The identified material constants were compared with vocal fold data reported in the literature. This method may be generalized to other types of soft tissues.