Feasibility of extracting tissue material properties via cohesive elements: a finite element approach to probe insertion procedures in non-invasive spine surgeries.

Abstract

Modeling the mechanical behavior of soft tissue probe insertion remains a challenging endeavor due to involved interdependent phenomena comprising tissue nonlinear deformation, contact between the probe and the tissue, crack propagation, and viscoelastic effects. To that matter, cohesive elements allow simulating crack formation and propagation, which provides a promising path to modeling the mechanical behavior of probe insertion in soft tissues. As such, the aim of the present study was to investigate the feasibility of devising and integrating an algorithm in a finite element (FE) case study in efforts of reverse engineering the material properties of non-homogeneous soft tissues. A layered nonlinear tissue model with a cohesive zone was created in the commercial software ABAQUS. Material properties were iteratively modified via a hybrid gradient descent optimization algorithm: minimizing the resultant error to first find optimum Ogden's hyperelastic parameters, followed by obtaining the damage parameters. Perceived material properties were then compared to those obtained via experimental human cadaver testing. Under the investigated four-layered muscle model, numerical results overlapped, to a great extent, with six different force-insertion experimental profiles with an average error of [Formula: see text] 15%. The best profile fit was realized when the highest sudden force drop was less than 60% of the peak force. Lastly, the FE analysis revealed an increase in stiffness as the probe advanced inside the tissue. The optimization algorithm demonstrated its capability to reverse engineer the material parameters required for the FE analysis of real, non-homogeneous, soft tissues. The significance of this procedure lies within its ability to extract tissue material parameters, in real time, with little to no intervention or invasive experimental tests. This could potentially further serve as a database for different muscle layers and force-insertion profiles, used for surgeon and physician clinical training purposes.