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对组织参数估计的方法不确定性进行比较,以计算碳离子剂量

Shutong Yu^1,2, Yan Li^2,3, Weiguang Li^2,3

¹Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.

Medical physics

|December 25, 2025

概括

基于机器学习的双能量CT (ML-DECT) 显著减少了碳离子放射治疗剂量计算中的不确定性. 这种方法提高了治疗计划和质量保证的准确性和可靠性.

科学领域:

医学物理医学物理
放射治疗物理物理
计算生物学计算生物学

背景情况:

精确的剂量计算对于有效和安全的碳离子放射治疗至关重要.
对于蒙特卡洛剂量计算的组织参数估计的不确定性可能会限制临床质量保证.

研究的目的:

系统地评估组织参数估计中的不确定性如何影响基于蒙特卡洛的碳离子剂量计算.
为了比较三个元素分解方法对它们对剂量分配的一致性和可靠性的影响.

主要方法:

使用单能CT (SECT),参数化双能CT (PA-DECT) 和基于机器学习的DECT (ML-DECT) 来传播物理密度和元素组成的不确定性.
在ICRP 110幻影中使用FLUKA模拟剂量分配,评估物理和生物剂量.
评估了voxel-wise不确定性,马传递率和范围不确定性.

主要成果:

与SECT相比,ML-DECT将C,N和O密度的不确定性降低了高达77.4%.
通过ML-DECT实现了较低的平均相对剂量不确定性 (∼5%物理,∼7%-9%生物) 和更高的马传递率 (97.96 ± 0.28%).
ML-DECT导致范围不确定性降低 (0.5%).

结论:

ML-DECT提高了组织参数估计的准确性,从而减少了基于蒙特卡洛的碳离子剂量计算中的不确定性.
这些发现支持将ML-DECT整合到治疗计划中,以便在碳离子放射治疗中提供更多的定量和不确定性意识质量保证.

关键词:

碳离子疗法是一种碳离子疗法.剂量的不确定性双能量CT是双能量CT.机器学习是机器学习.蒙特卡洛模拟游戏蒙特卡洛模拟游戏

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