Department of Radiation Oncology, Hospital of The University of Pennsylvania, Philadelphia, PA, 19104, USA.
Department of Atomic, Molecular and Nuclear Physics, Universidad de Sevilla, Seville, 41080, Spain.
Med Phys. 2019 Dec;46(12):5816-5823. doi: 10.1002/mp.13861. Epub 2019 Nov 4.
To introduce a new algorithm-MicroCalc-for dose calculation by modeling microdosimetric energy depositions and the spectral fluence at each point of a particle beam. Proton beams are considered as a particular case of the general methodology. By comparing the results obtained against Monte Carlo computations, we aim to validate the microdosimetric formalism presented here and in previous works.
In previous works, we developed a function on the energy for the average energy imparted to a microdosimetric site per event and a model to compute the energetic spectrum at each point of the patient image. The number of events in a voxel is estimated assuming a model in which the voxel is completely filled by microdosimetric sites. Then, dose at every voxel is computed by integrating the average energy imparted per event multiplied by the number of events per energy beam of the spectral distribution within the voxel. Our method is compared with the proton convolution superposition (PCS) algorithm implemented in Eclipse™ and the fast Monte Carlo code MCsquare, which is here considered the benchmark, for in-water calculations, using in both cases clinically validated beam data. Two clinical cases are considered: a brain and a prostate case.
For a SOBP beam in water, the mean difference at the central axis found for MicroCalc is of 0.86% against 1.03% for PCS. Three-dimensional gamma analyses in the PTVs compared with MCsquare for criterion (3%, 3 mm) provide gamma index of 95.07% with MicroCalc vs 94.50% with PCS for the brain case and 99.90% vs 100.00%, respectively, for the prostate case. For selected organs at risk in each case (brainstem and rectum), mean and maximum difference with respect to MCsquare dose are analyzed. In the brainstem, mean differences are 0.25 Gy (MicroCalc) vs 0.56 Gy (PCS), whereas for the rectum, these values are 0.05 Gy (MicroCalc) vs 0.07 Gy (PCS).
The accuracy of MicroCalc seems to be, at least, not inferior to PCS, showing similar or better agreement with MCsquare in the considered cases. Additionally, the algorithm enables simultaneous computation of other quantities of interest. These results seem to validate the microdosimetric methodology in which the algorithm is based on.
介绍一种新的算法-MicroCalc,通过模拟微剂量学能量沉积和粒子束中每个点的光谱通量来进行剂量计算。质子束被视为一般方法的特殊情况。通过将获得的结果与蒙特卡罗计算进行比较,我们旨在验证这里和以前的工作中提出的微剂量学形式。
在以前的工作中,我们开发了一个关于能量的函数,用于计算每个事件中微剂量学位点所赋予的平均能量,以及一个在患者图像的每个点计算能量谱的模型。假设一个模型,其中体素完全由微剂量学位点填充,来估计体素中的事件数量。然后,通过积分每个能量束的光谱分布内的平均能量乘以每个能量的事件数量来计算每个体素的剂量。我们的方法与 Eclipse™ 中实现的质子卷积叠加 (PCS) 算法和快速蒙特卡罗代码 MCsquare 进行比较,后者被认为是基准,用于水计算,在两种情况下都使用经过临床验证的光束数据。考虑了两个临床病例:一个大脑和一个前列腺病例。
对于水中的 SOBP 束,在中央轴上发现的 MicroCalc 的平均差异为 0.86%,而 PCS 的差异为 1.03%。在 PTV 中与 MCsquare 进行的三维伽马分析,对于标准(3%,3mm),对于大脑病例,MicroCalc 的伽马指数为 95.07%,PCS 为 94.50%,对于前列腺病例,分别为 99.90%和 100.00%。对于每个病例中的选定危险器官(脑干和直肠),分析了与 MCsquare 剂量的平均和最大差异。在脑干中,平均差异为 0.25Gy(MicroCalc)与 0.56Gy(PCS),而对于直肠,这些值为 0.05Gy(MicroCalc)与 0.07Gy(PCS)。
MicroCalc 的准确性似乎至少不逊于 PCS,在考虑的病例中与 MCsquare 具有相似或更好的一致性。此外,该算法能够同时计算其他感兴趣的数量。这些结果似乎验证了算法所基于的微剂量学方法。