Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4
Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4
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DOI：
10.1088/0031-9155/57/11/3281
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发表时间：
2012-06-07
影响因子：
3.5
通讯作者：
Perl, J.
中科院分区：
文献类型：
--
作者：
Schuemann, J.;Paganetti, H.;Perl, J.
所有蒙特卡罗粒子传输代码中的一个关键任务是“导航”，即计算确定粒子在每个粒子步骤中可能离开的体积和粒子可能进入的体积。导航应该针对手边的特定几何图形进行优化。对于患者剂量计算，这种几何结构通常涉及体素化计算机断层扫描（CT）数据。我们在蒙特卡罗模拟包Geant4中研究了当前可用体素几何参数化的导航算法的效率：G4VPVParameterisation， G4VNestedParameterisation和G4PhantomParameterisation，最后一个有和没有边界跳过，一种将具有相同Hounsfield单元的相邻体素组合成一个更大体素的方法。第四种参数化方法（MGHParameterization）也包括在本研究中，该方法是在Geant4中提供后两种参数化之前由内部开发的。所有的模拟都是使用TOPAS进行的，TOPAS是一种基于Geant4的粒子模拟工具。对三个不同的患者CT数据集进行了运行时比较：头部和颈部，肝脏和前列腺患者。我们纳入了这三个患者的附加版本，其中所有体素，包括患者外部的空气体素，在运行时研究中被统一设置为水。G4VPVParameterisation提供了两个优化选项。一个选项的模拟速度要慢60-150倍。另一种方法在速度上是兼容的，但需要比其他参数化多15-19倍的内存。我们发现，相对于G4VNestedParameterisation，用于模拟的平均CPU时间在G4PhantomParameterisation中为1.014，在没有边界跳过的情况下为1.015，在MGHParameterization中为1.015。对于我们的异构数据，G4PhantomParameterisation带和不带边界跳变的平均运行时比等于0.97:1。计算出的剂量分布与参考分布一致，除了G4PhantomParameterisation外，头颈部患者的边界跳变。最大内存使用量从0.8到1.8 GB不等，这取决于与参数化无关的CT体积，除了当使用具有更高模拟速度的选项时，G4VPVParameterisation的内存使用量增加了15-19倍。选择G4VNestedParameterisation作为研究的患者几何形状和治疗计划的首选。
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.