Deep Modelling of Electric Field Distribution for Clinical Electroporation Ablation Therapies

Electroporation ablation is a promising non-surgical and minimally invasive tumor ablation technique. The objective of this paper is to propose a deep learning strategy for fast and accurate 3D computation of the electric field distribution during the ablation procedure. The computation of the electrostatic potential is accelerated using a well-designed convolutional neural network, which serves to initialize the iterative solver for matrix inversion The proposed approach, which combine deep learning and more standard numerical strategies, is compared with state-of-the-art numerical schemes, evaluating its performance in terms of error on the solution, residuals of the numerical scheme, and computation time. As a proof of concept, we demonstrate its potential on a clinical case of electroporation ablation of a liver tumor, showing that the proposed approach can be advantageously combined with clinical data to provide real-time insights into the effective electroporation ablation.