A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning
A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning
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DOI：
10.1118/1.4875700
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发表时间：
2014-06-01
期刊：
影响因子：
3.8
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Zarepisheh, Masoud;Long, Troy;Jiang, Steve B.
目的：开发一种新的算法，将先前的治疗知识结合到调强放射治疗优化中，以促进自动治疗计划和自适应放射治疗（ART）重新计划。该算法自动创建由参考计划的DVH曲线引导的治疗计划，该参考计划包含关于不同目标之间的临床医生批准的剂量-体积权衡的信息。器官之间以及器官的DVH曲线的不同部分之间。在ART中，参考计划是同一患者的初始计划，而对于自动治疗计划，参考计划是从具有类似医疗状况和几何形状的先前治疗患者的临床批准和交付计划库中选择的。该算法采用基于体素的优化模型，并导航基于体素的大型Pareto曲面。迭代地调整体素权重以接近在DVH方面类似于参考计划的计划。如果参考计划是可行的，但不是Pareto最优的，该算法生成一个Pareto最优计划的DVH优于参考。如果参考计划对新几何结构的限制太大，则算法生成DVH接近参考DVH的Pareto计划。在这两种情况下，新的计划有类似的DVH权衡作为参考plannes.Results：该算法进行了测试，使用三个病人的情况下，发现能够自动调整的体素加权因子，以生成一个Pareto计划与类似的DVH权衡作为参考plannes. Results。该算法也已实现在GPU上的高效率。结论：一种新的先验知识为基础的优化算法已经开发，自动调整体素的权重，并在高效率的临床最佳计划。结果表明，该算法在ART再计划和自动治疗计划中能显著提高计划质量和计划效率。(C)2014年美国医学物理学家协会。
Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning.Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans.Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency.Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment planning. (C) 2014 American Association of Physicists in Medicine.