Chan Timothy C Y, Bortfeld Thomas, Tsitsiklis John N
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Phys Med Biol. 2006 May 21;51(10):2567-83. doi: 10.1088/0031-9155/51/10/014. Epub 2006 May 4.
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.
在放射治疗治疗计划中，应对不确定性是一项重大挑战，这包括由分次内运动引起的不确定性，对于胸部和腹部的肿瘤来说尤为重要。考虑运动的常用方法是引入边界或用运动概率密度函数对静态剂量分布进行卷积。与该领域之前的工作不同，我们的研究并不假定患者按照固定分布呼吸，也不要求患者在整个治疗过程中以相同方式呼吸。尽管具有这种通用性，我们从卷积方法出发创建了一个稳健的优化框架，该框架对呼吸运动的波动具有鲁棒性，并且比边界解决方案能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体模上测试我们的模型。在我们的数值结果中，稳健解决方案对健康组织的剂量比边界解决方案少约38%，同时提供相同水平的抗呼吸不确定性保护。
