A thermoelastic deformation model of tissue contraction during thermal ablation

Abstract

Purpose: Thermal ablation is an energy-based ablation technique widely used during minimally invasive cancer treatment. Simulations are used to predict the dead tissue post therapy. However, one difficulty with the simulations is accurately predicting the ablation zone in post-procedural images due to the contraction of tissue as a result of exposure to elevated temperatures.

Materials and methods: A mathematical model of the thermoelastic deformation for an elastic isotropic material was coupled with a three state thermal denaturation model to determine the contraction of tissue during thermal ablation. A finite difference method was considered to quantify the tissue contraction for a typical temperature distribution during thermal ablation.

Results: The simulations show that tissue displacement during thermal ablation was not bound to the tissue heated regions only. Both tissue expansion and contraction were observed at the different stages of the heating process. Tissue contraction of up to 42% was obtained with an applicator temperature of 90 °C. A recovery of around 2% was observed with heating removed as a result of unfolded state proteins returning back to its native state. Poisson’s ratio and the applicator temperature have both been shown to affect the tissue displacement significantly. The maximum tissue contraction was found to increase with both increasing Poisson’s ratio and temperature.

Conclusions: The model presented here will allow predictions of thermal ablation to be corrected for tissue contraction, which is an important effect, during comparison with post-procedural images, thus improving the accuracy of mathematical simulations for treatment planning.

Keywords:

• Thermal ablation
• thermal denaturation
• thermoelastic deformation model
• tissue contraction
• mathematical model

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research has been funded by the European Commission under Grant Agreement No. 600641, FP7 Project Go-Smart.