Optimization of importance factors in inverse planning
Optimization of importance factors in inverse planning
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DOI：
10.1088/0031-9155/44/10/311
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发表时间：
1999-10-01
影响因子：
3.5
通讯作者：
Boyer, AL
中科院分区：
文献类型：
--
作者：
Xing, L;Li, JG;Boyer, AL
逆处理规划从一个处理目标出发，通过优化目标函数得到解。临床目标通常是多方面的，并且可能彼此不相容。一组重要因素通常被纳入目标函数，以参数化权衡策略，并优先考虑不同解剖结构的剂量一致性。虽然一般的形式主义保持不变，但不同的重要因素使计划具有明显不同的风格，从而决定性地决定了最终的计划。到目前为止，这些参数的确定一直是一种基于经验知识的“猜测”游戏，因为最终剂量分布以复杂和隐含的方式依赖于这些参数。在方案优化完成之前，这些参数的影响是未知的。为了适当地折衷目标和敏感结构的相互冲突的要求，通常通过试错过程来调整参数。本文提出了一种计算估计这些参数的方法，并描述了一种迭代计算机算法来数值确定这些参数。治疗方案的选择分两步进行。首先，选取一组重要因子，在二次目标函数的指导下，采用先前报道的迭代算法对相应的光束参数（如光束轮廓）进行优化。“最优”计划然后通过一个额外的评分函数进行评估。对目标函数中的重要因子进行相应调整，提高方案的排名。每当重要因子发生变化时，都需要对梁参数进行重新优化。这个过程以迭代的方式继续，直到评分功能饱和。将该算法应用于两个临床案例，结果表明该算法具有明显改进现有逆规划方法的潜力。人们注意到，在接近最终解决方案时，该方案对重要因素的微小变化变得不敏感。
Inverse treatment planning starts with a treatment objective and obtains the solution by optimizing an objective function. The clinical objectives are usually multifaceted and potentially incompatible with one another. A set of importance factors is often incorporated in the objective function to parametrize trade-off strategies and to prioritize the dose conformality in different anatomical structures. Whereas the general formalism remains the same, different sets of importance factors characterize plans of obviously different flavour and thus critically determine the final plan. Up to now, the determination of these parameters has been a 'guessing' game based on empirical knowledge because the final dose distribution depends on the parameters in a complex and implicit way. The influence of these parameters is not known until the plan optimization is completed. In order to compromise properly the conflicting requirements of the target and sensitive structures, the parameters are usually adjusted through a trial-and-error process. In this paper, a method to estimate these parameters computationally is proposed and an iterative computer algorithm is described to determine these parameters numerically. The treatment plan selection is done in two steps. First, a set of importance factors are chosen and the corresponding beam parameters (e.g. beam profiles) are optimized under the guidance of a quadratic objective function using an iterative algorithm reported earlier. The 'optimal' plan is then evaluated by an additional scoring function. The importance factors in the objective function are accordingly adjusted to improve the ranking of the plan. For every change in the importance factors, the beam parameters need to be re-optimized. This process continues in an iterative fashion until the scoring function is saturated. The algorithm was applied to two clinical cases and the results demonstrated that it has the potential to improve significantly the existing method of inverse planning. It was noticed that near the final solution the plan became insensitive to small variations of the importance factors.