布拉格峰
剂量学
蒙特卡罗方法
离子
通量
Sobp公司
原子物理学
指数函数
质子疗法
物理
材料科学
核物理学
质子
计算物理学
辐照
核医学
数学
统计
数学分析
量子力学
医学
作者
S. Tuomanen,V Moskvin,J Farr
摘要
Purpose: Semi‐empirical modeling is a powerful computational method in radiation dosimetry. A set of approximations exist for proton ion depth dose distribution (DDD) in water. However, the modeling is more complicated for carbon ions due to fragmentation. This study addresses this by providing and evaluating a new methodology for DDD modeling of carbon ions in water. Methods: The FLUKA, Monte Carlo (MC) general‐purpose transport code was used for simulation of carbon DDDs for energies of 100–400 MeV in water as reference data model benchmarking. Based on Thomas Bortfeld's closed form equation approximating proton Bragg Curves as a basis, we derived the critical constants for a beam of Carbon ions by applying models of radiation transport by Lee et. al. and Geiger to our simulated Carbon curves. We hypothesized that including a new exponential (κ) residual distance parameter to Bortfeld's fluence reduction relation would improve DDD modeling for carbon ions. We are introducing an additional term to be added to Bortfeld's equation to describe fragmentation tail. This term accounts for the pre‐peak dose from nuclear fragments (NF). In the post peak region, the NF transport will be treated as new beams utilizing the Glauber model for interaction cross sections and the Abrasion‐ Ablation fragmentation model. Results: The carbon beam specific constants in the developed model were determined to be : p= 1.75, β=0.008 cm‐1, γ=0.6, α=0.0007 cm MeV, σmono=0.08, and the new exponential parameter κ=0.55. This produced a close match for the plateau part of the curve (max deviation 6.37%). Conclusion: The derived semi‐empirical model provides an accurate approximation of the MC simulated clinical carbon DDDs. This is the first direct semi‐empirical simulation for the dosimetry of therapeutic carbon ions. The accurate modeling of the NF tail in the carbon DDD will provide key insight into distal edge dose deposition formation.
科研通智能强力驱动
Strongly Powered by AbleSci AI