A row-generation approach for simultaneous multiple needle trajectory planning in radiofrequency ablation

Abstract

Radiofrequency ablation is a thermal therapy for moderately-sized cancerous tumors. A target is killed with high temperatures obtained due to the current passed through one or more electrodes (needles) inserted into it. The needles' trajectory must be meticulously planned to prevent interference with dense organs like bone or puncturing of critical structures like veins. By approximating the thermal lesion to an ellipse, we predefine several valid needle trajectories and then solve an integer programming model to identify pairwise valid needle positions, that meet clinical criteria, using a variation of the classic set cover model. To improve the models' tractability and scalability, we use row generation-based decomposition techniques that determines pairwise validity using two different types of cuts. Finally, we analyze target and organ-at-risk (OAR) damage using several thermal damage models. Our method is tested on 12 liver targets: three targets each with four different surgical margins. We show promising results that meet clinical guidelines while obtaining full target coverage.