Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing

Temperature and state-dependent electrical conductivity of soft biological tissue at hyperthermic temperatures

Objective: We present a physics-based, temperature and state-dependent electrical conductivity model for soft biological tissue under thermal therapies with a quantified damage parameter that represents the state of soft biological tissue (degree of denaturation). Most existing models consider electrical conductivity to be only temperature-dependent and evaluate tissue damage during post-processing after temperature calculation. Our model allows tissue damage to be coupled into the thermal model for a more accurate description of both RF ablation and electrosurgery. Methods: We model the denaturation process with an Arrhenius-type differential equation for chemical kinetics and a modified Stogryn equation for electrical conductivity under state transition. We present experimental data from two types of heating procedures at 128 kHz to validate and showcase the capability of our model. Results: Our model is able to capture the change in electrical conductivity during heating, cooling, and reheating procedures, which distinguishes different states and shows the irreversibility of denaturation. The model also accurately captures tissue change during slow cooking at a constant temperature, highlighting a state dependence. Conclusion: By incorporating state dependence into the model for electrical properties, we are able to capture the denaturation process more accurately and distinguish different degrees of damage. Our model allows the modeling of procedures involving repeated heating or cooling, which is impossible for models without a state dependence. While being able to adapt to patient-specific needs, the model can be used to improve planning and control in future robot-assisted surgeries to reduce unnecessary damage.

Heat conduction in live tissue during radiofrequency electrosurgery

In this paper, we propose a method to model radiofrequency electrosurgery to capture the phenomena at higher temperatures and present the methods for parameter estimation. Experimental data taken from our surgical trials performed on in vivo porcine liver show that a non-Fourier Maxwell-Cattaneo-type model can be suitable for this application when used in combination with an Arrhenius-type model that approximates the energy dissipation in physical and chemical reactions. The resulting model structure has the advantage of higher accuracy than existing ones, while reducing the computation time required.

Minimally Invasive Live Tissue High-Fidelity Thermophysical Modeling Using Real-Time Thermography

We present a novel thermodynamic parameter estimation framework for energy-based surgery on live tissue, with direct applications to tissue characterization during electrosurgery. This framework addresses the problem of estimating tissue-specific thermodynamics in real-time, which would enable accurate prediction of thermal damage impact to the tissue and damage-conscious planning of electrosurgical procedures. Our approach provides basic thermodynamic information such as thermal diffusivity, and also allows for obtaining the thermal relaxation time and a model of the heat source, yielding in real-time a controlled hyperbolic thermodynamics model. The latter accounts for the finite thermal propagation time necessary for modeling of the electrosurgical action, in which the probe motion speed often surpasses the speed of thermal propagation in the tissue operated on. Our approach relies solely on thermographer feedback and a knowledge of the power level and position of the electrosurgical pencil, imposing only very minor adjustments to normal electrosurgery to obtain a high-fidelity model of the tissue-probe interaction. Our method is minimally invasive and can be performed in situ. We apply our method first to simulated data based on porcine muscle tissue to verify its accuracy and then to in vivo liver tissue, and compare the results with those from the literature. This comparison shows that parameterizing the Maxwell-Cattaneo model through the framework proposed yields a noticeably higher fidelity real-time adaptable representation of the thermodynamic tissue response to the electrosurgical impact than currently available. A discussion on the differences between the live and the dead tissue thermodynamics is also provided.