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Simultaneous optimization of beam orientations and beam weights in conformal radiotherapy
Author(s) -
Rowbottom Carl Graham,
Khoo Vincent S.,
Webb Steve
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 - 10.1118/1.1387274
Subject(s) - beam (structure) , dosimetry , voxel , simulated annealing , optimization problem , computer science , mathematical optimization , nuclear medicine , mathematics , medicine , optics , physics , artificial intelligence
A methodology for the concurrent optimization of beam orientations and beam weights in conformal radiotherapy treatment planning has been developed and tested on a cohort of five patients. The algorithm is based on a beam‐weight optimization scheme with a downhill simplex optimization engine. The use of random voxels in the dose calculation provides much of the required speed up in the optimization process, and allows the simultaneous optimization of beam orientations and beam weights in a reasonable time. In the implementation of the beam‐weight optimization algorithm just 10% of the original patient voxels are used for the dose calculation and cost function evaluation. A fast simulated annealing algorithm controls the optimization of the beam arrangement. The optimization algorithm was able to produce clinically acceptable plans for the five patients in the cohort study. The algorithm equalized the dose to the optic nerves compared to the standard plans and reduced the mean dose to the brain stem by an average of 4.4% (±1.9, 1 SD), p value = 0.007 . The dose distribution to the PTV was not compromised by developing beam arrangements via the optimization algorithm. In conclusion, the simultaneous optimization of beam orientations and beam weights has been developed to be routinely used in a realistic time. The results of optimization in a small cohort study show that the optimization can reliably produce clinically acceptable dose distributions and may be able to improve dose distributions compared to those from a human planner.