Modeling of carbonization and stalling during radiofrequency ablation and electrosurgery

Radiofrequency (RF) thermal therapies, including RF ablation and electrosurgery, function by delivering alternating currents in the 100 kHz-1 MHz range to biological tissues. These currents generate electric fields that drive ionic oscillations in intracellular and extracellular fluids, producing localized resistive heating. Depending on the applied waveform, duty cycle, and exposure duration, this heating can vaporize, coagulate, or denature tissue, enabling dissection, vessel sealing, or tumor ablation with high spatial precision. RF ablation typically employs inserted electrodes to raise tumor temperatures to 60-100 °C for coagulative necrosis, while electrosurgery uses surface contact electrodes to achieve rapid and localized tissue modification for surgical cutting and hemostasis. Despite their broad clinical use, the physical mechanisms governing these processes remain incompletely understood, particularly under high-temperature conditions where denaturation, carbonization, and charring occur. Conventional thermal and electrical models assume instantaneous heat propagation and neglect relaxation effects, latent heat absorption, and irreversible biochemical reactions, resulting in limited predictive accuracy. Furthermore, existing models are computationally expensive, hindering real-time feedback control in modern robot-assisted electrosurgical systems. This dissertation presents a comprehensive, physics-based framework for modeling RF-induced thermal and electrical interactions in biological tissue with high accuracy and computational efficiency. The foundation of the framework is a non-Fourier Maxwell-Cattaneo heat conduction model, which accounts for finite relaxation times and delayed heat propagation. The model is coupled with an Arrhenius-type kinetic formulation to describe irreversible tissue denaturation, enabling the simulation of energy absorption due to physical and chemical transformations. Together, these models achieve an order-of-magnitude improvement in computational speed while maintaining predictive accuracy superior to traditional diffusion-based approaches. The work further extends the framework to include a temperature- and state-dependent electrical conductivity model that distinguishes among native, denatured, and coagulated tissue states. By incorporating a two-stage chemical kinetics model, the formulation captures the transition from denaturation to carbonization (or charring) and the associated sharp decrease in electrical conductivity that leads to electrosurgical stalling: a phenomenon where excessive energy deposition prevents the electrosurgical generator from sustaining adequate current flow. This capability represents a significant advancement, as prior models could not dynamically simulate the feedback-driven cessation of electrosurgical action. Numerical simulations demonstrate the emergence of multiscale thermal effects such as shock fronts, Mach wedges, and localized heating zones during rapid electrode motion. Stochastic modeling using Cauchy and Dagum random fields captures the influence of tissue heterogeneity on relaxation behavior and heat transport. Experimental validation using in vivo porcine liver and ex vivo porcine muscle confirms that the model accurately reproduces measured temperature fields, conductivity evolution, and tissue state transitions under both steady and transient heating conditions. Finally, the framework classifies electrosurgical operations into four regimes, identifying an optimal “sweet zone” where heat transfer effects are minimal and real-time computation is feasible. Under these conditions, a reduced order real-time capable model can achieve predictions of damage width that have error less than 1 mm, demonstrating both clinical relevance and computational tractability. Overall, this work establishes an experimentally validated, physics-based framework for radiofrequency ablation and electrosurgery that integrates the coupled thermal-electrical-chemical interactions governing tissue response and accurately reproduces the key phenomena of carbonization and stalling. Its balance of physical accuracy, computational speed, and modular adaptability establishes a foundation for real-time, patient-specific simulation and feedback control in next-generation robot-assisted and autonomous surgical systems.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Ran, Junren

Contributors dc:contributor

• Ostoja-Starzewski, Martin
• Bentsman, Joseph
• Chamorro, Leonardo P
• Berlin, Richard B

• Electrosurgery
• Thermal Therapies
• Heat Transfer
• Computational Mechanics

Statement dc:rights

• Copyright 2025 Junren Ran

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132653

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132653