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

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.