A hierarchical evolutionary algorithm for multiobjective optimization in IMRT
A hierarchical evolutionary algorithm for multiobjective optimization in IMRT
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DOI：
10.1118/1.3478276
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发表时间：
2010-09-01
期刊：
影响因子：
3.8
通讯作者：
Phillips, Mark H.
中科院分区：
文献类型：
--
作者：
Holdsworth, Clay;Kim, Minsun;Phillips, Mark H.
目的：现有的调强放射治疗（IMRT）逆向计划方法存在局限性，因为它们没有设计成探索肿瘤和正常组织之间的竞争目标之间的权衡。我们的目标是开发一个高效的多目标优化算法，是足够灵活的，以处理任何形式的目标函数，并导致在一组Pareto最优plans.Methods：一个层次的进化多目标算法，旨在快速生成一个小的不同的Pareto最优的一组IMRT计划，满足所有的临床约束条件，并反映在任何放射治疗计划的最佳权衡开发。分层算法的顶层是一个多目标进化算法（MOEA）。在MOEA中生成的个体的基因是定义在加速确定性IMRT优化期间最小化的惩罚函数的参数，该加速确定性IMRT优化表示层次结构的底层。MOEA结合临床标准，通过协议目标限制搜索空间，然后使用帕累托最优的健身目标之间选择个人。种群规模不是固定的，而是利用一种专门化的生态位效应--支配优势来控制种群和规划多样性。健身目标的数量保持在最低限度，更大的选择压力，但基因的数量是扩大的灵活性，允许更好的近似的Pareto front.Results：MOEA的改善进行了评估，为两个例子前列腺的情况下，一个目标和两个器官的风险（OARs）。人口的计划所产生的修改MOEA更接近帕累托前比人口的计划使用标准的遗传算法包。该方法的统计学意义是通过编译使用每种方法的25个多目标优化的结果来建立的。从这些12-15个方案的集合中，从MOEA群体中选择的任何随机方案具有11.3% +/-0.7%的机会支配通过标准遗传包选择的任何随机方案，具有0.04% +/-0.02%的反向支配机会。通过实施支配优势和协议目标，可以在1小时内生成接近帕累托前沿的临床可接受计划的小而多样的群体。两个层次的分层算法上实现的加速技术导致在短，实用的运行时间为multiobjective optimization.Conclusions：MOEA产生一个不同的帕累托最优的一组计划，满足所有剂量测定协议的标准在一个可行的时间。最终目标是改进算法的实用方面，并将其与决策分析工具或人机界面相结合，以选择IMRT计划，并在任何特定患者情况下成功治疗靶点、低OAR剂量和低并发症风险之间实现最佳平衡。(C)2010年美国医学物理学家协会。[DOI 10.1118/1.3478276]
Purpose: The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans.Methods: A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front.Results: The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12-15 plans, any random plan selected from a MOEA population had a 11.3% +/- 0.7% chance of dominating any random plan selected by a standard genetic package with 0.04% +/- 0.02% chance of domination in reverse. By implementing domination advantage and protocol objectives, small and diverse populations of clinically acceptable plans that approximated the Pareto front could be generated in a fraction of 1 h. Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for multiobjective optimizations.Conclusions: The MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. The final goal is to improve practical aspects of the algorithm and integrate it with a decision analysis tool or human interface for selection of the IMRT plan with the best possible balance of successful treatment of the target with low OAR dose and low risk of complication for any specific patient situation. (C) 2010 American Association of Physicists in Medicine. [DOI: 10.1118/1.3478276]