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The Gamma Knife (Elekta Instruments, Inc., Atlanta, 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 3D 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 which typically leads to suboptimal conformal target coverage by the dose. We have used adaptive simulated annealing/quenching followed by Nelder-Mead simplex optimization to automate the selection and placement of Gaussian-based `shots' to form a simulated dose plane. In order to make the computation of the problem tractable, the algorithm, based upon contouring and polygon clipping, takes a 2 1/2-D approach to defining the cost function. Several experiments have been performed where the optimizers have been given the freedom to vary the number of shots and the weight, collimator size, and 3D location of each shot. To data best results have been obtained by forcing the optimizers to use a fixed number of unweighted shots with each optimizer set free to vary the 3D location and collimator size of each shot. Our preliminary results indicate that this technology will radically decrease planning time while significantly increasing accuracy of conformal target coverage and reproducibility over current manual methods.
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