Objective

Monte Carlo (MC) simulations are considered to be the most accurate form of algorithm for dose calculation. However, the main obstacle to using MC, especially in clinical routine, is the simulation time needed to gain results below a desirable level of uncertainty. Variance reduction techniques (VRTs) have been introduced to reduce the simulation time while maintaining the uncertainty at an acceptable level. The aim of this study is to investigate and optimize the VRTs implemented in EGSnrc MC code, BEAMnrc and DOSXYZnrc.

Methodology

The BEAMnrc user code was used to model a 10 × 10 cm² field size of a 6 MV photon beam from an Elekta Synergy linear accelerator. The DOSXYZnrc user code was used to model a water phantom. The effects of different VRTs on the simulation efficiency were investigated either individually or in combination. The directional bremsstrahlung splitting (DBS) technique was investigated further to find the optimum splitting number and splitting field radius. For DOSXYZnrc, the photon splitting was investigated to find the best combination with the VRTs in BEAMnrc and to find the optimum splitting number. Finally, the best combination of VRTs in both BEAMnrc and DOSXYZnrc was compared with the corresponding phase space (PHSP) simulation source.

Results

The DBS technique was found to be the most efficient. The optimum splitting number was found to be 10,000 and 15,000 with and without electron splitting, respectively. For the DBS splitting field radius, overestimating by up to 3 cm would be sufficient without causing a significant loss in efficiency. For both BEAMnrc and DOSXYZnrc, the combination of DBS, bremsstrahlung cross-section enhancement, range rejection with 2 MeV and photon splitting (with optimum splitting number of 35) was the most efficient, and was about 8% less efficient than PHSP simulation.

Conclusion

The VRTs implemented in EGSnrc MC code made it possible to achieve an acceptably small uncertainty within a reasonable simulation time, if optimised properly. The combination of VRTs presented in this study eliminates the need to spare a large amount of disk space, and where parallel computing could allow for MC dose calculation in real-time adaptive treatment planning.

中文翻译：

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Elekta Synergy线性加速器的6 MV光子束在EGSnrc Monte Carlo中减少方差的技术的优化

客观的

蒙特卡洛（MC）模拟被认为是剂量计算算法的最精确形式。但是，使用MC的主要障碍（尤其是在临床常规中）是获得低于理想不确定度水平的结果所需的仿真时间。已引入方差降低技术（VRT）以减少仿真时间，同时将不确定性保持在可接受的水平。这项研究的目的是调查和优化在EGSnrc MC代码，BEAMnrc和DOSXYZnrc中实现的VRT。

方法

使用BEAMnrc用户代码对来自Elekta Synergy线性加速器的6 MV光子束的10×10 cm ²场大小进行建模。DOSXYZnrc用户代码用于对水体模型进行建模。单独或组合研究了不同VRT对仿真效率的影响。进一步研究了定向致裂分裂（DBS）技术，以找到最佳分裂数和分裂场半径。对于DOSXYZnrc，研究了光子分裂，以在BEAMnrc中找到与VRT的最佳组合，并找到最佳分裂数。最后，将BEAMnrc和DOSXYZnrc中VRT的最佳组合与相应的相空间（PHSP）仿真源进行了比较。

结果

发现DBS技术是最有效的。发现有和没有电子分裂的最佳分裂数分别为10,000和15,000。对于DBS分裂场半径，高估最多3 cm就足够了，而不会造成效率上的重大损失。对于BEAMnrc和DOSXYZnrc而言，DBS，致横截面增强，具有2 MeV的距离抑制和光子分裂（最佳分裂数为35）的组合是最有效的，并且比PHSP模拟的效率低约8％。

结论

如果进行了适当的优化，以EGSnrc MC代码实现的VRT可以在合理的仿真时间内实现可接受的较小不确定性。这项研究中提出的VRT的组合消除了节省大量磁盘空间的需要，并且在并行计算可以允许实时自适应治疗计划中计算MC剂量的地方。