Fast computation of soft tissue thermal response under deformation based on fast explicit dynamics finite element algorithm for surgical simulation

TL;DR

This paper introduces a fast explicit dynamics finite element algorithm to accurately compute soft tissue thermal response during deformation, enabling near real-time temperature prediction for surgical simulations and treatments.

Contribution

It presents a novel formulation for bio-heat transfer under tissue deformation using a transformation-based approach for rapid computation.

Findings

Achieves normalized relative error of 1.0e-3 compared to ABAQUS solutions.

Demonstrates effective thermal ablation simulation on a virtual human liver.

Provides a computationally efficient method suitable for real-time surgical applications.

Abstract

During thermal heating surgical procedures such as electrosurgery, thermal ablative treatment and hyperthermia, soft tissue deformation due to tool-tissue interaction and patients' motion can affect the distribution of induced thermal energy. Tissue temperature must be efficiently and accurately obtained from deformed tissues for precise thermal energy delivery; however, the classical Pennes bio-heat transfer can handle only the static non-moving state of soft tissue. This paper presents a formulation of bio-heat transfer under the effect of tissue deformation for fast or near real-time tissue temperature computation, based on fast explicit dynamics finite element algorithm for transient heat transfer. The proposed computation is achieved by transformation of the unknown deformed tissue state to the known initial non-moving state via a mapping function. The appropriateness and effectiveness of the proposed methodology are evaluated on a realistic virtual human liver with blood vessels to demonstrate a clinically relevant scenario of thermal ablation of hepatic cancer. Compared against the established non-linear procedures from commercial finite element analysis package, ABAQUS/CAE, the proposed methodology can achieve a typical 1.0e-3 level of normalized relative error at nodes and between 1.0e-4 and 1.0e-5 level of total errors, which is in good agreement with ABAQUS solutions. The proposed method consumes slightly more time than the formulation without soft tissue deformation, and computation performance of five different formulations are examined. The proposed method can be applied with bio-mechanical deformable models for fast or near real-time computation of non-linear bio-heat transfer, leading to translational potential in dynamic tissue temperature predictive analysis and thermal dosimetry computation for computer-integrated medical education and personalized treatments.