A parallel algorithm for generating Pareto‐optimal radiosurgery treatment plans

BACKGROUND: Using inverse planning tools to create radiosurgery treatment plans is an iterative process, where clinical trade-offs are explored by changing the relative importance given to different objectives and rerunning the optimizer until a desirable plan is found. Simultaneously generating many plans corresponding to different objective weights, while the patient is awaiting treatment, would allow the planner to navigate clinical trade-offs interactively, without optimizing a new plan between each update. PURPOSE: We seek to optimize hundreds of Gamma Knife radiosurgery treatment plans, corresponding to different weightings of objectives, fast enough to allow interactive Pareto navigation of clinical trade-offs to be incorporated into the clinical workflow. METHODS: . RESULTS: 3.0%, for coverage, selectivity, gradient index and beam-on time, respectively, compared to mean values for the corresponding reference simplex results. The standard deviations in these metrics closely mimicked those obtained when rerunning the simplex solver, verifying the validity of the method. CONCLUSIONS: We show how ADMM can be adapted for radiosurgery plan optimization, allowing hundreds of high-quality Gamma Knife treatment plans to be created in under two minutes on a single GPU, also for very large cases. The presented method would allow streamlined multicriteria optimization on the day of treatment, with interruption-free navigation of clinical trade-offs.