Premium
Study on optimization algorithms for intensity modulated radiation therapy
Author(s) -
Li Yongjie
Publication year - 2005
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 - 10.1118/1.1844091
Subject(s) - genetic algorithm , computer science , collimator , mathematical optimization , optimization problem , computation , dosimetry , algorithm , beam (structure) , mathematics , optics , physics , nuclear medicine , medicine
Aiming at the clinical requirements, this thesis recognizes conventional Intensity Modulated Radiation Therapy (IMRT) optimization techniques, i.e., the optimization of the beam intensity maps, which is the basis of other research works. Then research is done on beam angle optimization, on shortening optimization and saving treatment time, and so on, which are briefly listed below: (1) A comprehensive review is made on the current status of the IMRT, on the algorithms used in this thesis: the conjugate gradient method (CG) and the genetic algorithm (GA), and on the photon dose calculation algorithm. (2) Based on a physical objective function, a method for the optimization of beam intensity maps is developed using CG, among which some special steps are taken in order to improve the performance of the optimization. (3) Aiming to reduce the number of the segments and shorten the treatment time for static IMRT (step‐and‐shoot), the technique of GA‐based deliverable segment optimization (GADSO) is originated by the author. In GADSO, some genetic operations are designed, and also the multi‐leaf collimator physical constraints are incorporated. (4) In order to overcome the extensive computation of the currently available optimization methods, the author creatively develops automatic beam angle selection (ABAS) using the hybrid algorithm of GA and CG. In ABAS, the beam angles and the intensity maps are treated as two separated groups of variables and optimized using GA and CG, respectively. The efficiency of ABAS is achieved by taking some special measures, such as problem‐dependent genetic operations, immunity process, fitness scaling, and so on. (5) The author proposes and develops a framework for knowledge‐based beam angle optimization (KBASE), in which the plentiful clinical experiences accumulated over time by the physicists and oncologists are incorporated into the GA optimization procedure. In KBASE, two types of expert knowledge are utilized: (a) beam orientation constraints, which are used to reduce the angle searching space, and (b) template plans, which are mainly used to guide the GA genetic progress. Some preliminary results validate the presented KBASE.