Conference Paper

Ant colony system for the beam angle optimization problem in radiotherapy planning: A preliminary study

Sch. of Lfe Sci. & Technol., UESTC, Chengdu, China
DOI: 10.1109/CEC.2005.1554871 Conference: Evolutionary Computation, 2005. The 2005 IEEE Congress on, Volume: 2
Source: IEEE Xplore


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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    • "Another heuristic used to solve the BAO problem is an ant colony system. While in Li et al (2005b) a pure ant colony system is combined with a conjugate gradient algorithm, the same authors present a hybrid strategy combining a genetic algorithm and an ant colony system to solve the BAO problem in Li and Yao (2006). The authors propose this hybrid approach in order to obtain a balance between exploration and exploitation attributes of the hybrid algorithm. "

    No preview · Article · Jan 2016 · International Transactions in Operational Research
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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.
    No preview · Conference Paper · Sep 2006
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    ABSTRACT: Prostate cancer is the most common cancer in the male population. Radiotherapy is often used in the treatment for prostate cancer. In radiotherapy treatment, the oncologist makes a trade-off between the risk and benefit of the radiation, i.e., the task is to deliver a high dose to the prostate cancer cells and minimize side effects of the treatment. The aim of our research is to develop a software system that will assist the oncologist in planning new treatments. A nonlinear case-based reasoning system is developed to capture the expertise and experience of oncologists in treating previous patients. Importance (weights) of different clinical parameters in the dose planning is determined by the oncologist based on their past experience, and is highly subjective. The weights are usually fixed in the system. In this research, the weights are updated automatically each time after generating a treatment plan for a new patient using a group based simulated annealing approach. The developed approach is analyzed on the real data set collected from the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. Extensive experiments show that the dose plan suggested by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. The developed case-based reasoning system enables the use of knowledge and experience gained by the oncologist in treating new patients. This system may play a vital role to assist the oncologist in making a better decision in less computational time; it utilizes the success rate of the previously treated patients and it can also be used in teaching and training processes.
    Full-text · Article · Dec 2011 · Medical Physics
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