Vasi Fabiano, Schmidli Kevin, Hälg Roger A, Schneider Uwe
Radiotherapy Hirslanden, Witellikerstrasse 40, 8032, Zurich, Switzerland.
Department of Physic, University of Zurich, Winterthurerstrasse 190, 8032, Zurich, Switzerland.
Med Phys. 2020 Nov;47(11):5872-5881. doi: 10.1002/mp.14178. Epub 2020 Oct 13.
In view of the potential of treatment plan optimization based on nanodosimetric quantities, fast Monte Carlo methods for obtaining nanodosimetric quantities in macroscopic volumes are important. In this work, a "fast" method for obtaining nanodosimetric parameters from a clinical proton pencil beam in a macroscopic volume is compared with a slow and detailed method. Furthermore, the variations of these parameters, when obtained with the Monte Carlo codes TOPAS and NOREC, are investigated.
Monte Carlo track structure simulations of 1 keV-100 MeV protons and 12 eV-1 MeV electrons in a volume of 8 nm liquid water provided us with an atlas of cluster size distributions. Two kinds of ionization cluster size distributions were recorded, counting all ionizations or only ionizations directly produced by the primary particle. The simulations of the proton pencil beam were performed in two different ways. A "fast" method where only the protons were simulated and a "slow and detailed" method where protons and electrons were simulated in order to obtain spectra at different depths. The obtained spectra were then convoluted with cluster size distributions.
It was shown that the nanodosimetric quantity from the "fast" method is, depending on the location, between 43.6% and 63.6% smaller than the obtained by the "slow and detailed" method. However, it was also shown that variations of nanodosimetric quantities are even larger when the cluster size distributions of the electrons are simulated with the Monte Carlo code NOREC, that is, the cumulative probabilities obtained with NOREC were between 50.8% and 75.5% smaller than the probabilities obtained with TOPAS.
As long as the uncertainties of different Monte Carlo codes are not improved, it is feasible to only simulate protons in a macroscopic volume. It must be noted, however, that the uncertainty is in the order of 100%.
鉴于基于纳米剂量学量进行治疗计划优化的潜力，用于在宏观体积中获取纳米剂量学量的快速蒙特卡罗方法非常重要。在这项工作中，将一种从宏观体积中的临床质子笔形束获取纳米剂量学参数的“快速”方法与一种缓慢且详细的方法进行了比较。此外，还研究了使用蒙特卡罗代码TOPAS和NOREC获取这些参数时的变化情况。
对能量范围为1 keV至100 MeV的质子和12 eV至1 MeV的电子在8纳米液态水体积中的蒙特卡罗径迹结构模拟，为我们提供了簇尺寸分布图谱。记录了两种电离簇尺寸分布，一种是计算所有电离，另一种是仅计算由初级粒子直接产生的电离。质子笔形束的模拟以两种不同方式进行。一种“快速”方法是仅模拟质子，另一种“缓慢且详细”的方法是同时模拟质子和电子以获取不同深度处的能谱。然后将获得的能谱与簇尺寸分布进行卷积。
结果表明，“快速”方法得到的纳米剂量学量，根据位置不同，比“缓慢且详细”方法得到的量小43.6%至63.6%。然而，还表明当使用蒙特卡罗代码NOREC模拟电子的簇尺寸分布时，纳米剂量学量的变化甚至更大，即使用NOREC获得的累积概率比使用TOPAS获得的概率小50.8%至75.5%。
只要不同蒙特卡罗代码的不确定性没有得到改善，在宏观体积中仅模拟质子是可行的。然而，必须注意的是，不确定性在100%左右。
