Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
Phys Med Biol. 2012 Jun 7;57(11):3281-93. doi: 10.1088/0031-9155/57/11/3281. Epub 2012 May 9.
A key task within all Monte Carlo particle transport codes is 'navigation', the calculation to determine at each particle step what volume the particle may be leaving and what volume the particle may be entering. Navigation should be optimized to the specific geometry at hand. For patient dose calculation, this geometry generally involves voxelized computed tomography (CT) data. We investigated the efficiency of navigation algorithms on currently available voxel geometry parameterizations in the Monte Carlo simulation package Geant4: G4VPVParameterisation, G4VNestedParameterisation and G4PhantomParameterisation, the last with and without boundary skipping, a method where neighboring voxels with the same Hounsfield unit are combined into one larger voxel. A fourth parameterization approach (MGHParameterization), developed in-house before the latter two parameterizations became available in Geant4, was also included in this study. All simulations were performed using TOPAS, a tool for particle simulations layered on top of Geant4. Runtime comparisons were made on three distinct patient CT data sets: a head and neck, a liver and a prostate patient. We included an additional version of these three patients where all voxels, including the air voxels outside of the patient, were uniformly set to water in the runtime study. The G4VPVParameterisation offers two optimization options. One option has a 60-150 times slower simulation speed. The other is compatible in speed but requires 15-19 times more memory compared to the other parameterizations. We found the average CPU time used for the simulation relative to G4VNestedParameterisation to be 1.014 for G4PhantomParameterisation without boundary skipping and 1.015 for MGHParameterization. The average runtime ratio for G4PhantomParameterisation with and without boundary skipping for our heterogeneous data was equal to 0.97: 1. The calculated dose distributions agreed with the reference distribution for all but the G4PhantomParameterisation with boundary skipping for the head and neck patient. The maximum memory usage ranged from 0.8 to 1.8 GB depending on the CT volume independent of parameterizations, except for the 15-19 times greater memory usage with the G4VPVParameterisation when using the option with a higher simulation speed. The G4VNestedParameterisation was selected as the preferred choice for the patient geometries and treatment plans studied.
在所有蒙特卡罗粒子输运代码中，一个关键任务是“导航”，即计算粒子在每一步可能离开的体积和可能进入的体积。导航应该针对当前的特定几何形状进行优化。对于患者剂量计算，这种几何形状通常涉及体素化计算机断层扫描（CT）数据。我们研究了在蒙特卡罗模拟包 Geant4 中当前可用的体素几何参数化的导航算法的效率：G4VPVParameterisation、G4VNestedParameterisation 和 G4PhantomParameterisation，最后一个带有和不带有边界跳过，这是一种将具有相同亨氏单位的相邻体素组合成一个更大体素的方法。在 Geant4 中可用的后两种参数化方法之前，我们还开发了第四种参数化方法（MGHParameterization），也包括在本研究中。所有模拟都是使用 TOPAS 进行的，TOPAS 是一个位于 Geant4 之上的粒子模拟工具。在三个不同的患者 CT 数据集上进行了运行时比较：头部和颈部、肝脏和前列腺患者。我们还包括了这些三个患者的一个附加版本，其中所有体素，包括患者外部的空气体素，在运行时研究中都均匀地设置为水。G4VPVParameterisation 提供了两种优化选项。一种选项的模拟速度慢 60-150 倍。另一种选项在速度上兼容，但与其他参数化相比，需要 15-19 倍的内存。我们发现，相对于 G4VNestedParameterisation，模拟使用的平均 CPU 时间对于没有边界跳过的 G4PhantomParameterisation 为 1.014，对于 MGHParameterization 为 1.015。对于我们的异构数据，G4PhantomParameterisation 有和没有边界跳过的平均运行时比率相等，为 0.97:1。除了头部和颈部患者的 G4PhantomParameterisation 带有边界跳过的情况外，所有患者的计算剂量分布都与参考分布一致。最大内存使用量范围为 0.8 到 1.8GB，与 CT 体积无关，除了使用更高模拟速度选项时 G4VPVParameterisation 的 15-19 倍更大内存使用量。G4VNestedParameterisation 被选为研究的患者几何形状和治疗计划的首选。
