In vivo mechanical response of thigh soft tissues under compression: a two-layer model allows an improved representation of the local tissue kinematics

Abstract

Biomechanical parameters have the potential to be used as physical markers for prevention and diagnosis. Finite Element Analysis (FEA) is a widely used tool to evaluate these parameters in vivo. However, the development of clinically relevant FEA requires personalisation of the geometry, boundary conditions, and constitutive parameters. This contribution focuses on the characterisation of mechanical properties in vivo which remains a significant challenge for the community. The aim of this retrospective study is to evaluate the sensitivity of the computed elastic parameters (shear modulus of fat and muscle tissues) derived by inverse analysis as a function of the geometrical modelling assumption (homogenised monolayer vs bilayer) and the formulation of the cost function. The methodology presented here proposes to extract the experimental force-displacement response for each tissue layer (muscle and fat) and construct the associated Finite Element Model for each volunteer, based on data previously collected in our group (N=7 volunteers) as reported in (Fougeron et al., 2020). The sensitivity analysis indicates that the choice of the cost function has minimal impact on the topology of the response surface in the parametric space. Each surface displays a valley of parameters that minimises the cost function. The constitutive properties of the thigh (reported as median ± interquartile range) were determined to be (μ=198±322 kPa,α=37) for the monolayer and (μ_(muscle )=1675±1127 kPa,α_muscle=22±14,μ_fat=537±1131 kPa,α_fat=32±7) for the bilayer. A comparison of the homogenised monolayer and bilayer models showed that adding a layer reduces the error on the local force displacement curves, increasing the accuracy of the local kinematics of soft tissues during indentation. This allows for an increased understanding of load transmission in soft tissue. The comparison of the two models in terms of strains indicates that the modelling choice significantly influences the localization of maximal compressive strains. These results support the idea that the biomechanical community should conduct further work to develop reliable methodologies for estimating in vivo strain in soft tissue.