Ant colony system for the beam angle optimization problem in radiotherapy planning: a preliminary study
ABSTRACT Intensity-modulated radiotherapy (IMRT) is being increasingly used for treatment of malignant cancer. Beam angle optimization (BAO) is an important problem in IMRT. In this paper, an emerging population-based meta-heuristic algorithm named ant colony optimization (ACO) is introduced to solve the BAO problem. In the proposed algorithm, a multi-layered graph is designed to map the BAO problem to ACO, and a heuristic function based on the beam's-eye-view dosimetrics (BEVD) score is introduced. In order to verify the feasibility of the presented algorithm, a clinical prostate tumor case is employed, and the preliminary results demonstrate that ACO appears more effcient than genetic algorithm (GA) and can find the optimal beam angles within a clinically acceptable computation time.
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ABSTRACT: Computer-aided radiotherapy planning within a clinically acceptable time has the potential to improve the therapeutic ratio by providing the optimized and customized treatment plans for the tumor patients. In this paper, a hybrid method is proposed to accelerate the beam angle optimization (BAO) in the intensity modulated radiotherapy (IMRT) planning. In this hybrid method, the genetic algorithm (GA) is used to find the rough distribution of the solution, i.e., to give the initial pheromone distribution for the following ant colony system (ACS) optimization. Then, the ACS optimization is implemented to find the precise solution of the BAO problem. The comparisons of the optimization on a clinical nasopharynx case with GA, ACS and the hybrid method show that the proposed algorithm can obviously improve the computation efficiency.Advances in Natural Computation, Second International Conference, ICNC 2006, Xi'an, China, September 24-28, 2006. Proceedings, Part II; 01/2006
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ABSTRACT: This paper presents a decision support system for radiotherapy treatment planning for head, neck and brain cancer. The aim of a treatment plan is to apply radiation to kill tumor cells, while minimizing the damage to healthy tissue and critical organs. Since treatment planning is a complex decision making process that relies heavily on the subjective experience of clinicians, we propose the use of case-based reasoning (CBR), in which problems are solved based on the solutions of similar past problems. This paper focuses on the case retrieval process of a CBR system. The attributes, which describe the cases, are selected by assessing their effect on the performance of the CBR system. We have developed a context sensitive local weighting scheme that assigns weights to attributes based on their value and the values of other attributes in the target case. A novel two phase retrieval mechanism is developed, in which each phase is optimized to retrieve a particular part of the solution. We also present an original use of fuzzy logic in order to represent nonlinearity in the similarity measure. Experiments, which evaluate the similarity measure using real brain cancer patient cases, show promising results.International Journal of Artificial Intelligence Tools 08/2012; 21(04). DOI:10.1142/S0218213012400179 · 0.39 Impact Factor