PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning
PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning
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DOI：
10.1118/1.3615622
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发表时间：
2011-09-01
期刊：
影响因子：
3.8
通讯作者：
Cull, Andrew
中科院分区：
文献类型：
--
作者：
Fiege, Jason;McCurdy, Boyd;Cull, Andrew
目的：在放射疗法治疗计划中，对计划靶体积（PTV）的均匀高剂量和对危及器官（OAR）的低剂量的临床目标总是冲突的，在为特定患者选择最佳治疗计划时，通常需要在它们之间做出妥协。在这项工作中，作者介绍了帕累托意识的放射治疗进化治疗优化（PARETO），一个多目标优化工具，以解决调强放射治疗（IMRT）治疗计划中的射束角度和注量模式。pareto是围绕一个强大的多目标遗传算法（GA），这使我们能够把IMRT治疗计划优化的问题作为一个组合的整体问题，其中在优化过程中所有的射束注量和角度参数被同等地处理。我们采用了一个简单的参数化束流通量表示与现实的剂量计算方法，将患者的散射效应，以证明所提出的方法在两个幻影的可行性。第一个幻影是一个简单的圆柱形幻影包含一个目标包围三个OARs，而第二个幻影是更复杂的，代表一个paraspinal patient.Results：帕累托结果在一个大型数据库的帕累托非支配的解决方案，代表目标之间的必要权衡。几个PTV和OAR健身功能的解决方案的质量进行了检查。基于适形性的PTV适应度函数和剂量体积直方图（DVH）或基于等效均匀剂量（EUD）的OAR适应度函数的组合产生了相对均匀和适形的PTV剂量，具有良好的间隔射束。添加到适应度函数的惩罚函数消除了热点。将所得DVH与使用单目标注量优化器（来自商业治疗计划系统）开发的治疗计划的DVH进行比较，显示出良好的相关性。结果还表明，帕累托显示承诺优化beams.Conclusions的数量：IMRT治疗计划的进化优化软件工具帕累托的初步评估证明了可行性，并提供了持续发展的动力。这种方法相对于目前用于治疗计划的商业方法的优点有很多，包括：（1）完全自动化的优化，其避免了人为控制的迭代优化并且潜在地提高了整个过程效率，（2）将问题公式化为真正的多目标问题，它提供了一组优化的Pareto非支配解决方案，这些解决方案经过数百代的改进，并从探索的数千个参数集中编译而成在运行期间，以及（3）通过用于为患者选择最佳治疗选项的图形界面来完成最终非支配集的快速探索。(C)2011年美国医学物理学家协会。[DOI 10.1118/1.3615622]
Purpose: In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (PARETO), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning.Methods: pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient.Results: pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number of beams.Conclusions: This initial evaluation of the evolutionary optimization software tool pareto for IMRT treatment planning demonstrates feasibility and provides motivation for continued development. Advantages of this approach over current commercial methods for treatment planning are many, including: (1) fully automated optimization that avoids human controlled iterative optimization and potentially improves overall process efficiency, (2) formulation of the problem as a true multiobjective one, which provides an optimized set of Pareto nondominated solutions refined over hundreds of generations and compiled from thousands of parameter sets explored during the run, and (3) rapid exploration of the final nondominated set accomplished by a graphical interface used to select the best treatment option for the patient. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3615622]