Investigating multi-objective fluence and beam orientation IMRT optimization
Investigating multi-objective fluence and beam orientation IMRT optimization
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DOI：
10.1088/1361-6560/aa7298
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发表时间：
2017-06
影响因子：
3.5
通讯作者：
P. Potrebko;J. Fiege;M. Biagioli;J. Poleszczuk
中科院分区：
文献类型：
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作者：
P. Potrebko;J. Fiege;M. Biagioli;J. Poleszczuk
放射肿瘤学治疗计划需要在总是相互冲突的临床目标之间做出妥协。对整个治疗计划有一个“鸟瞰”视角是有益的，这些计划代表了向计划目标体积（PTV）提供预期剂量与最佳保护危及器官（OAR）之间可能的权衡。在这项工作中，作者展示了帕累托感知放射治疗进化治疗优化（PARETO），这是一种具有鸟瞰功能的多目标工具，可优化调强放射治疗（IMRT）治疗计划的注量模式和射束角度。 IMRT 治疗计划优化问题作为一个组合整体问题进行管理，其中所有射束注量和角度参数在优化过程中都得到同等对待。为了实现这一目标，PARETO 围绕一种名为 Ferret 的强大多目标进化算法构建，该算法同时优化多个适应度函数，这些函数对 PTV 和 OAR 所需剂量分布的属性进行编码。 PARETO 内的图形界面提供有用的信息，例如：优化期间的收敛行为、竞争目标之间的权衡图以及最佳解决方案数据库的图形表示，允许通过评估剂量体积直方图和等剂量分布来快速探索治疗计划质量。 PARETO 针对两个相对复杂的临床病例（鼻旁窦病例和胰腺病例）进行了评估。每次 PARETO 运行的最终结果是一个最优（非支配）治疗计划的数据库，该数据库证明了 OAR 和 PTV 适应度函数之间的权衡，这些函数在帕累托最优意义上都同样好（其中任何一个目标都不能在不恶化至少另一个目标的情况下得到改善）。即使优化中包含大量参数（例如光束注量和光束角度），Ferret 仍能够生成高质量的解决方案。
Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters, such as beam fluence and beam angles, were included in the optimization.