A stochastic approach to full inverse treatment planning for charged-particle therapy

Charged-particle therapy is a rapidly growing precision radiotherapy technique that treats tumors with ion beams. Because ion-beam delivery systems have multiple degrees of freedom (including the beam trajectories, energies and fluences), it can be extremely difficult to find a treatment plan that accurately matches the dose prescribed to the tumor while sparing nearby healthy structures. This inverse problem is called inverse treatment planning (ITP). Many ITP approaches have been proposed for the simpler case of X-ray therapy, but the work dedicated to charged-particle therapy is usually limited to optimizing the beam fluences given the trajectories and energies. To fill this gap, we consider the problem of simultaneously optimizing the beam trajectories, energies, and fluences, which we call full ITP. The solutions are the global minima of an objective function defined on a very large search space and having deep local basins of attraction; because of this difficulty, full ITP has not been studied (except in preliminary work of ours). We provide a proof of concept for full ITP by showing that it can be solved efficiently using simulated annealing (SA). The core of our work is the incremental design of a state exploration mechanism that substantially speeds up SA without altering its global convergence properties. We also propose an original approach to tuning the cooling schedule, a task critical to the performance of SA. Experiments with different irradiation configurations and increasingly sophisticated SA algorithms demonstrate the benefits and potential of the proposed methodology, opening new horizons to charged-particle therapy.