A Deep Learning Surrogate Model for Microwave Thermal Ablation: From Preoperative Planning to Intraoperative Re-planning

Overview & Synopsis

With the increasing interest in thermal ablation therapies, patient-specific predictive simulations have become crucial for improving both preoperative planning and intraoperative corrections. Planning strategies typically rely on the solution of an inverse problem, yet high-fidelity finite element simulations remain computationally prohibitive when embedded within an optimization loop under strict clinical time constraints. In this work, we introduce a real-time deep learning surrogate model for predicting microwave ablation outcomes, trained exclusively on synthetic data and personalized through 3D perfusion maps that encode patient anatomy and local perfusion rates. This surrogate model takes as input the antenna position, power and ablation duration, and outputs the predicted necrosis volume within a few milliseconds. Once integrated into an optimization loop, it becomes possible to propose antenna trajectories and ablation settings that maximize tumor coverage while minimizing damage to healthy tissue. We demonstrate this on clinical data via a physics-based optimization framework that computes optimal ablation parameters in under 2 minutes, critical for preoperative planning or intraoperative re-planning in microwave ablation. The surrogate model was validated on in vivo pre-clinical data where it achieved an average Dice coefficient of 0.82 for necrosis mask prediction.