Lan Yihua, Ren Haozheng, Li Cunhua, Min Zhifang, Wan Jinxin, Ma Jianxin, Hung Chih-Cheng
School of Computer and Information Technology, Nanyang Normal University, Nanyang, Henan 473061, P.R.China.
Technol Cancer Res Treat. 2013 Oct;12(5):391-401. doi: 10.7785/tcrt.2012.500339. Epub 2013 Apr 24.
In order to facilitate the leaf sequencing process in intensity modulated radiation therapy (IMRT), and design of a practical leaf sequencing algorithm, it is an important issue to smooth the planned fluence maps. The objective is to achieve both high-efficiency and high-precision dose delivering by considering characteristics of leaf sequencing process. The key factor which affects total number of monitor units for the leaf sequencing optimization process is the max flow value of the digraph which formulated from the fluence maps. Therefore, we believe that one strategy for compromising dose conformity and total number of monitor units in dose delivery is to balance the dose distribution function and the max flow value mentioned above. However, there are too many paths in the digraph, and we don't know the flow value of which path is the maximum. The maximum flow value among the horizontal paths was selected and used in the objective function of the fluence map optimization to formulate the model. The model is a traditional linear constrained quadratic optimization model which can be solved by interior point method easily. We believe that the smoothed maps from this model are more suitable for leaf sequencing optimization process than other smoothing models. A clinical head-neck case and a prostate case were tested and compared using our proposed model and the smoothing model which is based on the minimization of total variance. The optimization results with the same level of total number of monitor units (TNMU) show that the fluence maps obtained from our model have much better dose performance for the target/non-target region than the maps from total variance based on the smoothing model. This indicates that our model achieves better dose distribution when the algorithm suppresses the TNMU at the same level. Although we have just used the max flow value of the horizontal paths in the diagraph in the objective function, a good balance has been achieved between the dose conformity and the total number of monitor units. This idea can be extended to other fluence map optimization model, and we believe it can also achieve good performance.
为了便于调强放射治疗（IMRT）中的叶片排序过程，并设计一种实用的叶片排序算法，平滑计划的注量图是一个重要问题。目标是通过考虑叶片排序过程的特性来实现高效和高精度的剂量输送。影响叶片排序优化过程中监测单元总数的关键因素是由注量图构建的有向图的最大流值。因此，我们认为在剂量输送中兼顾剂量适形性和监测单元总数的一种策略是平衡上述剂量分布函数和最大流值。然而，有向图中的路径太多，我们不知道哪条路径的流值最大。选择水平路径中的最大流值并将其用于注量图优化的目标函数中以构建模型。该模型是一个传统的线性约束二次优化模型，可以通过内点法轻松求解。我们认为，与其他平滑模型相比，该模型得到的平滑图更适合叶片排序优化过程。使用我们提出的模型和基于总方差最小化的平滑模型对一个临床头颈病例和一个前列腺病例进行了测试和比较。在相同监测单元总数（TNMU）水平下的优化结果表明，与基于总方差平滑模型得到的图相比，我们的模型得到的注量图在靶区/非靶区具有更好的剂量性能。这表明当算法在相同水平抑制TNMU时，我们的模型实现了更好的剂量分布。尽管我们仅在目标函数中使用了有向图中水平路径的最大流值，但在剂量适形性和监测单元总数之间实现了良好的平衡。这个想法可以扩展到其他注量图优化模型，我们相信它也能取得良好的性能。
