Automated Gamma Knife dose planning using polygon clipping and adaptive simulated annealing

The Gamma Knife (Elekta Instruments, Inc., Norcross, GA), a neurosurgical, highly focused radiation delivery device, is used to eradicate deep‐seated anomalous tissue within the human brain by delivering a lethal dose of radiation to target tissue. This dose is the accumulated result of delivering sequential “shots” of radiation to the target, where each shot is approximately three‐dimensional (3‐D) Gaussian in shape. The size and intensity of each shot can be adjusted by varying the time of radiation exposure and by using one of four collimator sizes ranging from 4–18 mm. Current dose planning requires that the dose plan be developed manually to cover the target, and only the target, with a desired minimum radiation intensity using a minimum number of shots. This is a laborious and subjective process that typically leads to suboptimal conformal target coverage by the dose. We have previously presented a forward—direct—method, which, using adaptive simulated annealing and Nelder–Mead simplex optimizers, automates the selection and placement of generic Gaussian‐based kernels or “shots” to form a simulated dose plan. In order to make the computation of the problem tractable, the algorithm exploits 2‐D contouring and polygon clipping and takes a 21 2‐D approach to defining the problem. In the current paper we present the results of four experiments on two historical clinical datasets, where the generic kernels have been replaced by patient specific kernels calculated by Elekta's Leksell Gamma Plan software. For these experiments the user only selects the maximum number of shots to use and the optimizers are then given the freedom to vary the number of shots as well as the weight, collimator size, and 3‐D location of each shot. Highly conformal and competitive dose plans were generated for these two difficult cases.