Optimization of Gamma Knife treatment planning via guided evolutionary simulated annealing

We present a method for generating optimized Gamma Knife (TM) (Elekta, Stoc kholm, Sweden) radiosurgery treatment plans. This semiautomatic method prod uces a highly conformal shot packing plan for the irradiation of an intracr anial tumor. We simulate optimal treatment planning criteria with a probabi lity function that is linked to every voxel in a volumetric (MR or CT) regi on of interest. This sigmoidal P+ parameter models the requirement of confo rmality (i.e., tumor ablation and normal tissue sparing). After determinati on of initial radiosurgery treatment parameters, a guided. evolutionary sim ulated annealing (GESA) algorithm is used to find the optimal size, positio n, and weight for each shot. The three-dimensional GESA algorithm searches the shot parameter space more thoroughly than is possible during manual sho t packing and provides one plan that is suitable to the treatment criteria of the attending neurosurgeon and radiation oncologist. The result is a mor e conformal plan, which also reduces redundancy, and saves treatment admini stration time. (C) 2001 American Association of Physicists in Medicine.