| Radiotherapy, together with the surgery and chemotherapy, are the three main means for tumor treatment. It is a historic advancement for tumor treatment that the classical three-dimensional (3D) conformal radiotherapy (3DCRT) evolved into the intensity-modulated radiotherapy (IMRT). Whereas, the advantages of IMRT have not yet been fully utilized, because of the complicated clinical conditions. The IMRT planning, one of the key issues of IMRT application, still has many problems needed to be dealt with.Aiming at the clinical requirements, this thesis realizes the conventional IMRT optimization technique, i.e. the optimization of the beam intensity maps, which is the base of the other researching works. Then researches are done on the 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, including the shortcomings of the current IMRT optimization algorithms and the clinical requirements. Also, a summarization is done to the algorithms used in this thesis: conjugate gradient method (CG) and genetic algorithm (GA), and a brief description is given to the photon dose calculation algorithm. These review and summarization are essential preparation for the coming researches.(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. The results of simulated and clinical tumor cases demonstrate that the proposed algorithm is valid and timesaving. The algorithm has been embedded into a commercial TPS system for routine IMRT planning. (3) Aiming to reduce the number of the segments and shorten the treatment time for static IMRT (step-&-shoot), the technique of GA-based deliverable segment optimization (GADSO) is originated by the author. In GADSO, some novel genetic operations are designed, and also the MLC physical constraints are incorporated. GADSO has been published on Phys. Med. Biol., and is appraised as the first work to introduce GA into the segment optimization.(4) The author creatively develops the automatic beam angle selection (ABAS) using the hybrid algorithm of GA and CG, in order to overcome the extensive computation of the currently available optimization methods. 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-dependant genetic operations, immunity process, fitness scaling, and so on. Some clinical applications show that ABAS is valid and feasible. ABAS has been accepted as a paper by Phys. Med. Biol..(5) As the first one, the author proposes and develops a framework for the knowledge-based beam angle optimization (KBASE), in which the plentiful clinical experiences accumulated over time by the physicists 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.(6) As a supplementary work, the clinical dose verification is made for Vairan MLC and Siemens MLC, and the comparison results are shown for the point doses and film dose distributions, among which good agreement is achieved.
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