Suppr超能文献

在调强质子治疗中使用二次目标函数进行稳健优化以考虑最小监测单位约束。

Robust optimization in IMPT using quadratic objective functions to account for the minimum MU constraint.

作者信息

Shan Jie, An Yu, Bues Martin, Schild Steven E, Liu Wei

机构信息

Department of Biomedical Informatics, Arizona State University, Tempe, AZ, USA.

Department of Radiation Oncology, Mayo Clinic Hospital, Phoenix, AZ, USA.

出版信息

Med Phys. 2018 Jan;45(1):460-469. doi: 10.1002/mp.12677. Epub 2017 Dec 5.

Abstract

PURPOSE

Currently, in clinical practice of intensity-modulated proton therapy (IMPT), the influence of the minimum monitor unit (MU) constraint is taken into account through postprocessing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization.

METHODS AND MATERIALS

Cao et al. have demonstrated a two-stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using seven cancer patients with different machine settings.

RESULT

The new method achieved better plan quality than the conventional method. The D of the clinical target volume (CTV) normalized to the prescription dose was (mean [min-max]): (99.4% [99.2%-99.6%]) vs. (99.2% [98.6%-99.6%]). Plan robustness derived from these two methods was comparable. For all seven patients, the CTV dose-volume histogram band gap (narrower band gap means more robust plans) at D normalized to the prescription dose was (mean [min-max]): (1.5% [0.5%-4.3%]) vs. (1.2% [0.6%-3.8%]).

CONCLUSION

Our new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine-deliverable plans with better tumor coverage while maintaining high-plan robustness.

摘要

目的

目前,在调强质子治疗(IMPT)的临床实践中,最小监测单位(MU)约束的影响是在优化完成后通过后处理来考虑的。这可能会降低计划质量和计划稳健性。本研究旨在在计划稳健优化过程中直接减轻最小MU约束的影响。

方法和材料

Cao等人展示了一种两阶段方法,用于在不考虑不确定性影响的情况下使用线性规划来考虑最小MU约束。在本研究中,我们使用二次优化来考虑最小MU约束,并同时使用稳健优化来考虑不确定性的影响。我们使用七名具有不同机器设置的癌症患者评估了我们的方法。

结果

新方法比传统方法实现了更好的计划质量。临床靶体积(CTV)归一化到处方剂量的D值为(平均值[最小值 - 最大值]):(99.4% [99.2% - 99.6%])对(99.2% [98.6% - 99.6%])。这两种方法得出的计划稳健性相当。对于所有七名患者,CTV剂量体积直方图带隙(带隙越窄意味着计划越稳健)在归一化到处方剂量的D值处为(平均值[最小值 - 最大值]):(1.5% [0.5% - 4.3%])对(1.2% [0.6% - 3.8%])。

结论

我们将最小MU约束直接纳入计划稳健优化的新方法可以生成机器可交付的计划,在保持高计划稳健性的同时具有更好的肿瘤覆盖。

相似文献

引用本文的文献

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验