A robust approach to IMRT optimization
A robust approach to IMRT optimization
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DOI：
10.1088/0031-9155/51/10/014
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发表时间：
2006-05-21
影响因子：
3.5
通讯作者：
Tsitsiklis, JN
中科院分区：
文献类型：
--
作者：
Chan, TCY;Bortfeld, T;Tsitsiklis, JN
管理不确定性是放射治疗计划的主要挑战，包括由屈光运动引起的不确定性，这对胸腔和腹部的肿瘤尤其重要。解释运动的常用方法是引入边界或将静态剂量分布与运动概率密度函数进行卷积。与该领域以前的工作不同，我们的研究并不假设患者按照固定的分布呼吸，也不要求患者在整个治疗过程中以相同的方式呼吸。尽管有这种通用性，但我们从卷积方法开始创建了一个鲁棒的优化框架，该框架对呼吸运动的波动具有鲁棒性，但比边缘解决方案更好地保护健康组织。我们描述了如何使用呼吸运动数据为我们的模型生成数据，并使用来自真实患者的数据在计算机幻影上测试我们的模型。在我们的数值结果中，稳健溶液对健康组织的剂量比边缘溶液少约38%，同时提供相同水平的呼吸不确定性保护。
Managing uncertainty is a major challenge in radiation therapy treatment planning, including uncertainty induced by intrafraction motion, which is particularly important for tumours in the thorax and abdomen. Common methods to account for motion are to introduce a margin or to convolve the static dose distribution with a motion probability density function. Unlike previous work in this area, our development does not assume that the patient breathes according to a fixed distribution, nor is the patient required to breathe the same way throughout the treatment. Despite this generality, we create a robust optimization framework starting from the convolution method that is robust to fluctuations in breathing motion, yet spares healthy tissue better than a margin solution. We describe how to generate the data for our model using breathing motion data and we test our model on a computer phantom using data from real patients. In our numerical results, the robust solution delivers approximately 38% less dose to the healthy tissue than the margin solution, while providing the same level of protection against breathing uncertainty.