首页> 外文会议>International conference on swarm intelligence >Feasibility of an Ant Colony Optimization Algorithm for Multi-leaf Collimator (MLC) Aperture Definition and Beam Weighting in Volumetric Modulated Arc Therapy (VMAT) Radiotherapy Treatment Planning
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Feasibility of an Ant Colony Optimization Algorithm for Multi-leaf Collimator (MLC) Aperture Definition and Beam Weighting in Volumetric Modulated Arc Therapy (VMAT) Radiotherapy Treatment Planning

机译:体积调制弧光治疗(VMAT)放射治疗计划中多叶准直器(MLC)孔径定义和波束加权的蚁群优化算法的可行性

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Volumetric Modulated Arc Therapy (VMAT) is a sophisticated radiotherapy treatment delivery modality in which a medical linear accelerator arcs around a patient, with concurrent dynamic variation of multi-leaf collimator aperture, dose rate, and gantry speed, to produce a radiation dose distribution which delivers a highly conformal dose to the target while minimizing the incidental irradiation of normal tissue. Treatment planning for VMAT is an inverse problem, requiring optimization of the linear accelerator parameters to produce the desired radiation dose distribution, which is specified by dose-volume objectives. In this study, the feasibility of an ant colony algorithm for VMAT treatment planning is demonstrated by the ability of the algorithm to produce a treatment plan, which satisfies given dose-volume objectives, for a phantom target/critical structure geometry. Three experiments were conducted: one in which the optimization included only heuristic information, one in which there was exclusively a pheromone trail update, and one where there was both a pheromone trail update and an applied heuristic. The results indicate that the use of both a pheromone trail update and heuristic information during the optimization yields solutions of the highest quality.
机译:容积调制电弧疗法(VMAT)是一种先进的放射疗法治疗交付方式,其中医用线性加速器在患者周围成弧形,同时多叶准直器孔径,剂量率和门架速度同时发生动态变化,以产生辐射剂量分布,向靶标提供高保形剂量,同时将正常组织的偶然照射降至最低。 VMAT的治疗计划是一个反问题,需要优化线性加速器参数以产生所需的辐射剂量分布,这由剂量-体积目标确定。在这项研究中,蚁群算法用于VMAT治疗计划的可行性由该算法针对幻影目标/关键结构几何体生成满足给定剂量-体积目标的治疗计划的能力证明。进行了三个实验:一个实验中的优化仅包含启发式信息,一个实验中仅包含信息素追踪更新,另一个实验中既包含信息素追踪更新又应用了启发式。结果表明,在优化过程中同时使用信息素跟踪更新和启发式信息可产生最高质量的解决方案。

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