Optimization and evaluation of liver deformation modeling under microwave ablation based on ex vivo data

<i>Objective</i>. Microwave ablation (MWA) has emerged as a crucial therapeutic technique for treating hepatocellular carcinoma. Despite its effectiveness, temperature-dependent structural modifications in liver tissues can adversely affect outcomes. Numerical modeling and simulations are essential tools for predicting tissue temperature and deformation prediction. However, existing methods lack comprehensive consideration of deformation-causative factors and fail to validate accuracy throughout the ablation zone.<i>Approach</i>. To overcome these limitations, we analyzed the gap between the<i>ex vivo</i>ablation deformation results and numerical simulations, and combined them to optimize the physical fields of thermally induced deformation across the entire liver tissue ablation zone. Specifically, we employed a grid marker arrangement with delayed computed tomography (CT) imaging in<i>ex vivo</i>experiments to capture high-resolution global deformation data. The optimization of the simulation was based on updating the coefficient for protein denaturation shrinkage and incorporating vapor diffusion influence in the mechanical model. The effect of vapor diffusion was thoroughly investigated and modeled into the stress-strain equation.<i>Main results</i>. Evaluation results demonstrate that our method significantly improves simulation alignment with observed experimental data, enhancing prediction accuracy of tissue deformation by 30%-90%. Additionally, our model exhibits enhanced capability for expansion representation to describe localized region deformation, resulting in increases of 2.2%-10.0% in dice similarity coefficient (<i>DICE</i>) and 4.2%-19.0% in intersection over union (<i>IoU</i>) when quantifying morphological differences with<i>ex vivo</i>experimental results.<i>Significance</i>. The improved simulation modeling could benefit the planning and optimization of MWA procedures, potentially enhancing treatment efficacy.