Improving Prediction of the Potential Distribution Induced by Cylindrical Electrodes within a Homogeneous Rectangular Grid during Irreversible Electroporation

Pierre Agnass, Krijn van Lienden, Thomas van Gulik, Marc Besselink, Johannes Crezee, H. Petra Kok
2022 Applied Sciences  
Irreversible electroporation (IRE) is an ablation technique based on the application of short, high-voltage pulses between needle electrodes (diameter: ~1.0 × 10−3 m). A Finite Difference-based software simulating IRE treatment generally uses rectangular grids, yielding discretization issues when modeling cylindrical electrodes and potentially affecting the validity of treatment planning simulations. Aim: Develop an Electric-Potential Estimation (EPE) method for accurate prediction of the
more » ... ic-potential distribution in the vicinity of cylindrical electrodes. Methods: The electric-potential values in the voxels neighboring the cylindrical electrode voxels were corrected based on analytical solutions derived for coaxial/cylindrical electrodes. Simulations at varying grid resolutions were validated using analytical models. Low-resolution heterogeneous simulations at 2.0 × 10−3 m excluding/including EPE were compared with high-resolution results at 0.25 × 10−3 m. Results: EPE significantly reduced maximal errors compared to analytical results for the electric-potential distributions (26.6–71.8%→0.4%) and for the electrical resistance (30%→1–6%) at 3.0 × 10−3 m voxel-size. EPE significantly improved the mean-deviation (43.1–52.8%→13.0–24.3%) and the calculation-time gain (>15,000×) of low-resolution compared to high-resolution heterogeneous simulations. Conclusions: EPE can accurately predict the potential distribution of neighboring cylindrical electrodes, regardless of size, position, and orientation in a rectangular grid. The simulation time of treatment planning can therefore be shortened by using large voxel-sized models without affecting accuracy of the electric-field distribution, enabling real-time clinical IRE treatment planning.
doi:10.3390/app12031471 fatcat:zxfbbpqi3bhzljmi3fu6o3ph44