Spatial Porosity as a Diagnostic Predictor of Conductivity Collapse in Patient-Specific Radiofrequency Ablation of Liver Tumors

Background: Radiofrequency ablation of liver tumors relies on tightly coupled electromagnetic–thermal dynamics. However, conventional computational models oversimplify tissue heterogeneity and the dynamic evolution of biophysical properties, limiting their intraoperative diagnostic utility. Methods: We developed a patient-specific, three-dimensional multiphysics framework for liver RFA that integrates spatially varying tissue porosity with a modified local thermal equilibrium formulation. Advective heat transfer is computed via a supplementary finite-element equation, fully coupled with quasi-static electromagnetic simulations and Arrhenius-based tissue damage kinetics. Results: Simulations revealed three distinct voltage-dependent regimes: stable thermal–electromagnetic coupling at 50 V, optimal lesion expansion at 75 V, and premature electrical conductivity collapse at 100 V. Dynamic conductivity reduction, driven by dehydration and coagulative necrosis, provides a mechanistic basis for interpreting real-time impedance rises as an early indicator of peri-electrode desiccation. Geometry-constrained porosity mapping accurately reproduced anisotropic lesion morphologies, yielding simulated necrotic diameters of 2.8 ± 0.4 cm, closely aligning with MRI-validated clinical benchmarks. Conclusions: By linking microstructural heterogeneity to electromagnetic feedback, this framework transforms intraoperative impedance monitoring into a quantitative, predictive diagnostic tool. Imaging-derived spatial porosity mapping represents a robust biomarker for patient-specific liver RFA planning, significantly reducing procedural uncertainty and improving ablation precision.