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A Thermal Simulation Model and Validation for Percutaneous Cancer Ablation


G Deshazer

G Deshazer1*, D Dupuy2 , D Merck3 , (1) University of Rhode Island, Kingston, RI, (2,3) Rhode Island Hospital, Providence, RI

Presentations

SU-E-J-18 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:

Image-guided percutaneous ablation is an effective, inexpensive, and accessible treatment for liver, lung, and kidney cancers. However, because of its relatively high recurrence rate, percutaneous ablation is usually considered to be palliative only. We hypothesize that the high recurrence rate is due in part to poor margin control resulting from inaccurate treatment models in procedure planning. To address this shortcoming, we are developing a thermal simulation model for percutaneous cancer ablation from first principles.

Methods:

Our model uses finite element methods to solve Penne’s Heat Equation and Maxwell’s equations for electromagnetic energy transfer in matter over time. The simulation accounts for applicator geometry and materials as taken from actual clinical devices, and incorporates appropriate density, thermal conductivity properties, dielectric properties, power dissipation, and the relative water content for a homogeneous liver tissue domain.

Results:

The solution generated is a continuous 3D thermal profile over time. The simulated 60 degree Celsius isotherm at 60 watts for 10 minutes in homogenous liver tissue is accurate to within approximately 5% (i.e., an average 2mm variance from a 36mm expected width) of the vendor specification and within 12% (5mm) of our own ex vivo ablation procedures as measured by 4D CT and visual inspection of the coagulation zone. Simulations at other input settings give similar results within 5%-15% of expected and empirically observed results.

Conclusion:

This promising initial model forms the basis of ongoing work in principled thermal simulation for ablation planning. Our next goals are to incorporate heterogeneous tissue types and heat sinks in the solution domain and validate the resulting simulation against our ongoing clinical data collection.


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