Mixed variable optimization for radio frequency ablation planning

We present a method towards optimization of multiple ablation probe placement to provide efficient coverage of a tumor for thermal therapy while respecting clinical needs such as limiting the sites of probe insertions at the pleura/liver surface, choosing secure probe trajectories and locations, avoiding ablation of critical structures, reducing ablation of healthy tissue and overlap of ablation zones. The ablation optimizer treats each ablation location independently, and the number of ablation probe placements itself is treated as a variable to be optimized. This allows us to potentially feedback the ablation after deployment and re-optimize the next steps during the plan. The optimization method uses a new class of derivate-free algorithms for solving a non-linear mixed variable problem with hard and soft constraints derived from clinical images. Our methods use discretization of the ablation volume, which can accommodate irregular shape of the ablation zone. The non-gradient based strategy produce new candidates to yield a feasible solution within a few iterations. In our simulation experiments this strategy typically reduced the ablation zone overlap and ablated healthy tissue ablated by 46% and 29%, respectively in a single iteration, resulting in a feasible solution to be found within 35 iterations. Our method for optimization provides efficient implementation for planning the coverage of a tumor while respecting clinical constraints. The ablation planning can be combined with navigation assistance to enable accurate translation and feedback of the plan.