Application of micro-computer tomography and inverse finite element analysis for characterizing the visco-hyperelastic response of bulk liver tissue using indentation

• Main Authors: Rajeswara R. Resapu, Roger D. Bradshaw
• Format: Article
• Language: English
• Published: Springer 2021-04-01
• Series: SN Applied Sciences
• Subjects: Shear response; ANSYS; Nonlinear; Loading rate; Mapped mesh
• Online Access: https://doi.org/10.1007/s42452-021-04577-6

Abstract In-vitro mechanical indentation experimentation is performed on bulk liver tissue of lamb to characterize its nonlinear material behaviour. The material response is characterized by a visco-hyperelastic material model by the use of 2-dimensional inverse finite element (FE) analysis. The time-dependent behaviour is characterized by the viscoelastic model represented by a 4-parameter Prony series, whereas the large deformations are modelled using the hyperelastic Neo-Hookean model. The shear response described by the initial and final shear moduli and the corresponding Prony series parameters are optimized using ANSYS with the Root Mean Square (RMS) error being the objective function. Optimized material properties are validated using experimental results obtained under different loading histories. To study the efficacy of a 2D model, a three dimensional (3D) model of the specimen is developed using Micro-CT of the specimen. The initial elastic modulus of the lamb liver obtained was found to 13.5 kPa for 5% indentation depth at a loading rate of 1 mm/sec for 1-cycle. These properties are able to predict the response at 8.33% depth and a loading rate of 5 mm/sec at multiple cycles with reasonable accuracy. Article highlights The visco-hyperelastic model accurately models the large displacement as well as the time-dependent behaviour of the bulk liver tissue. Mapped meshing of the 3D FE model saves computational time and captures localized displacement in an accurate manner. The 2D axisymmetric model while predicting the force response of the bulk tissue, cannot predict the localized deformations.