AI 中文总结
研究质子剂量测定中硅-水剂量转换和布拉格峰指标对阻止本领数据集的敏感性，用PHITS模拟不同阻止本领数据，发现其对转换剂量大小影响强于布拉格峰位置，结果应纳入相关不确定度预算。
AI 中文摘要
准确的质子剂量测定需要用于探测器到水转换和蒙特卡罗辐射传输的一致阻止本领数据。我们量化了水到硅的阻止本领比、转换后的水剂量分布以及布拉格峰指标对水中67.5MeV原始质子束阻止本领数据集的敏感性。使用PHITS模拟，结合SRIM - 2013、PSTAR/NIST、ATIMA和TDDFT - Penn在高达10MeV的阻止本领以及更高能量下基于SBETHE的通用扩展。水到硅的阻止本领比显示相对入口到远端变化为17.40% - 22.10%。转换归一化的PTW硅二极管百分比深度电离数据产生相对百分比深度剂量曲线，其共同最大值在36.86mm，与实验布拉格峰深度(36.81 ± 0.15)mm一致。转换后的曲线在整个入口和平原区域比参考值低约3%，而在布拉格峰附近偏差高达2.1%。直接PHITS计算预测布拉格峰深度为36.575 - 36.675mm；只有TDDFT - Penn结果在引用的实验不确定度范围内。这些结果表明，阻止本领处理对转换剂量大小的影响比布拉格峰位置更强，并且应在质子束校准、调试、质量保证和蒙特卡罗验证的不确定度预算中进行协调或明确纳入。
英文摘要
Accurate proton dosimetry requires consistent stopping-power data for detector-to-water conversion and Monte Carlo radiation transport. We quantify the sensitivity of water-to-silicon stopping-power ratios, converted dose-to-water distributions, and Bragg-peak metrics to the stopping-power dataset for a 67.5 MeV pristine proton beam in water. PHITS simulations used SRIM-2013, PSTAR/NIST, ATIMA, and TDDFT-Penn stopping powers up to 10 MeV, together with a common SBETHE-based extension at higher energies. The water-to-silicon stopping-power ratio showed relative entrance-to-distal variations of 17.40%$-$22.10%. Conversion of normalized PTW silicon-diode percentage-depth-ionization data yielded relative percentage-depth-dose curves with a common maximum at 36.86 mm, consistent with the experimental Bragg-peak depth of (36.81 $\pm$ 0.15) mm. The converted curves were approximately 3% below the reference throughout the entrance and plateau regions, while deviations of up to 2.1% occurred near the Bragg peak. Direct PHITS calculations predicted Bragg-peak depths of 36.575$-$36.675 mm; only the TDDFT-Penn result lay within the quoted experimental uncertainty. These results show that stopping-power treatment affects the magnitude of the converted dose more strongly than the Bragg-peak position does, and that it should be harmonized or explicitly included in uncertainty budgets for proton-beam calibration, commissioning, quality assurance, and Monte Carlo validation.
