Enhancement of the EGSnrc code egs_chamber for fast fluence calculations of charged particles

Zeitschrift für Medizinische Physik(2022)

引用 2|浏览0
暂无评分
摘要
Purpose Simulation of absorbed dose deposition in a detector is one of the key tasks of Monte Carlo (MC) dosimetry methodology. Recent publications (Hartmann and Zink, 2018; Hartmann and Zink, 2019; Hartmann et al., 2021) have shown that knowledge of the charged particle fluence differential in energy contributing to absorbed dose is useful to provide enhanced insight on how response depends on detector properties. While some EGSnrc MC codes provide output of charged particle spectra, they are often restricted in setup options or limited in calculation efficiency. For detector simulations, a promising approach is to upgrade the EGSnrc code egs_chamber which so far does not offer charged particle calculations. Methods Since the user code cavity offers charged particle fluence calculation, the underlying algorithm was embedded in egs_chamber. The modified code was tested against two EGSnrc applications and DOSXYZnrc which was modified accordingly by one of the authors. Furthermore, the gain in efficiency achieved by photon cross section enhancement was determined quantitatively. Results Electron and positron fluence spectra and restricted cema calculated by egs_chamber agreed well with the compared applications thus demonstrating the feasibility of the new code. Additionally, variance reduction techniques are now applicable also for fluence calculations. Depending on the simulation setup, considerable gains in efficiency were obtained by photon cross section enhancement. Conclusion The enhanced egs_chamber code represents a valuable tool to investigate the response of detectors with respect to absorbed dose and fluence distribution and the perturbation caused by the detector in a reasonable computation time. By using intermediate phase space scoring, egs_chamber offers parallel calculation of charged particle fluence spectra for different detector configurations in one single run.
更多
查看译文
关键词
Monte Carlo simulations,EGSnrc,Variance reduction techniques,Charged particle fluence
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要