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A Heuristic Based on Fuzzy Inference Systems for Multiobjective IMRT Treatment Planning

机译:基于模糊推理的启发式多目标IMRT治疗计划

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Radiotherapy is one of the treatments used against cancer. Each treatment has to be planned considering the medical prescription for each specific patient and the information contained in the patient's medical images. The medical prescription usually is composed by a set of dosimetry constraints, imposing maximum or minimum radiation doses that should be satisfied. Treatment planning is a trial-and-error time consuming process, where the planner has to tune several parameters (like weights and bounds) until an admissible plan is found. Radiotherapy treatment planning can be interpreted as a multiobjective optimization problem, because besides the set of dosimetry constraints there are also several conflicting objectives: maximizing the dose deposited in the volumes to treat and, at the same time, minimizing the dose delivered to healthy cells. In this paper we present a new multiobjective optimization procedure that will, in an automated way, calculate a set of potential non-dominated treatment plans. It is also possible to consider an interactive procedure whenever the planner wants to explore new regions in the non-dominated frontier. The optimization procedure is based on fuzzy inference systems. The new methodology is described and it is applied to a head-and-neck cancer case.
机译:放射疗法是用于抗癌的治疗方法之一。必须考虑每个特定患者的医疗处方以及患者医学图像中包含的信息来计划每种治疗。该医疗处方通常由一组剂量学约束组成,强加应满足的最大或最小辐射剂量。治疗计划是一个反复试验的耗时过程,计划者必须调整几个参数(例如权重和界限),直到找到可以接受的计划。放疗治疗计划可以解释为一个多目标优化问题,因为除了剂量学约束集之外,还有几个相互矛盾的目标:最大化沉积在待治疗体积中的剂量,同时最小化向健康细胞的剂量。在本文中,我们提出了一种新的多目标优化程序,该程序将以自动化方式计算一组潜在的非支配性治疗计划。每当计划者想要在非主导边界中探索新区域时,也可以考虑采用交互式程序。优化过程基于模糊推理系统。描述了新方法,并将其应用于头颈癌病例。

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