Numerical study to establish relationship between coagulation volume and target tip temperature during...

Area of Science:

• Biomedical Engineering
• Medical Physics
• Computational Biology

Background:

• Radiofrequency ablation (RFA) is a minimally invasive procedure used to treat various medical conditions by generating heat to destroy targeted tissue.
• Accurate prediction of coagulation volume is crucial for effective RFA treatment and minimizing collateral damage.
• Understanding the relationship between ablation parameters and tissue response is essential for optimizing RFA protocols.

Purpose of the Study:

• To establish a relationship between coagulation volume and target tip temperature in different tissues (liver, lung, kidney, breast) during temperature-controlled RFA.
• To develop statistical correlations between coagulation volume, treatment time, and target tip temperature for various tissues.
• To evaluate the accuracy of a finite element model in predicting RFA outcomes through comparison with experimental data.

Main Methods:

• A 3D finite element model was developed to simulate temperature-controlled RFA, incorporating coupled electric field, Pennes bioheat, and Arrhenius rate equations.
• The model included temperature-dependent electrical and thermal conductivities and a non-linear blood perfusion model.
• Numerical simulations (20) and in vitro experiments (12) were conducted using a monopolar multi-tine electrode with target tip temperatures ranging from 70°C to 100°C.

Main Results:

• Coagulation volume demonstrated a strong dependence on the pre-set target tip temperature and ablation time.
• Statistical correlations were developed linking coagulation volume and treatment time at different target tip temperatures for each tissue type.
• The numerical model showed good agreement with in vitro experimental results, validating its predictive capability.

Conclusions:

• Target tip temperature and ablation time are critical factors influencing coagulation volume in RFA across different organs.
• The developed correlations provide a valuable tool for predicting RFA outcomes and optimizing treatment parameters for specific tissues.
• This computational approach enhances the understanding and precision of RFA procedures.