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Optimization of Gamma Knife treatment planning via guided evolutionary simulated annealing
Author(s) -
Zhang Pengpeng,
Dean David,
Metzger Andrew,
Sibata Claudio
Publication year - 2001
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1386427
Subject(s) - simulated annealing , radiosurgery , radiation treatment planning , conformal map , voxel , gamma knife , dosimetry , redundancy (engineering) , computer science , nuclear medicine , medical physics , algorithm , radiation therapy , mathematics , physics , medicine , artificial intelligence , radiology , mathematical analysis , operating system
We present a method for generating optimized Gamma Knife™ (Elekta, Stockholm, Sweden) radiosurgery treatment plans. This semiautomatic method produces a highly conformal shot packing plan for the irradiation of an intracranial tumor. We simulate optimal treatment planning criteria with a probability function that is linked to every voxel in a volumetric (MR or CT) region of interest. This sigmoidal P +parameter models the requirement of conformality (i.e., tumor ablation and normal tissue sparing). After determination of initial radiosurgery treatment parameters, a guided evolutionary simulated annealing (GESA) algorithm is used to find the optimal size, position, and weight for each shot. The three‐dimensional GESA algorithm searches the shot parameter space more thoroughly than is possible during manual shot packing and provides one plan that is suitable to the treatment criteria of the attending neurosurgeon and radiation oncologist. The result is a more conformal plan, which also reduces redundancy, and saves treatment administration time.