Automatic trajectory planning for stereotactic radiofrequency ablation in non-discrete search space

Radiofrequency ablation is a well established minimally invasive procedure to treat tumors in solid organs. During the procedure applicators are inserted into the tumor and cells around their tips are destroyed by heat-induced denaturation. Manual trajectory planning requires a trained interventionalist, and its complexity and planning time rise significantly with an increasing number of trajectories.

We propose a trajectory planning method using a genetic algorithm to accelerate the planning process by automatically generating multiple safe plans. Our method uses a non-discrete search space to find the best entry and target points and does not need any prior calculation of such candidate's points sets. The method offers multiple plans, allowing the interventionalists to choose the most appropriate one. We tested on an open-source and in-house dataset, comparing with related work and retrospectively with the in-house clinical planning.

Our method, tested on 154 liver tumors across all segments using a 10 mm ablation radius, achieves a mean coverage of over 99% of the tumors including a 5 mm safety margin. The method provides safe trajectories for all solutions and is on average 4 $\times$ faster than related approaches.

To the best of our knowledge, we are the first to propose a fast and accurate planning technique using multiple applicators with 10 mm ablation radius. Our algorithm can deliver solutions optimizing more than ten trajectories, approaching the clinical practice at our institution, where large tumors are treated with multiple overlapping ablation zones rather than resection.