从基向量模型重量推导组织特性,用于基于CT的双能量蒙特卡洛质子束剂量计算
ArXiv
|September 29, 2025
概括
一种新的方法,基本向量模型材料索引 (BVM-MI),改善了蒙特卡洛 (MC) 模拟中的质子剂量计算. BVM-MI准确地预测了组织组成和密度,优于传统的Hounsfield单位方法 (HU-MI) 提高了质子疗法规划.
科学领域:
- 医学物理 医学物理
- 放射治疗 物理 物理
- 计算成像技术的成像
背景情况:
- 准确的材料表征对于精确的蒙特卡洛 (MC) 剂量规划在质子疗法中至关重要.
- 传统的霍恩斯菲尔德单位材料索引 (HU-MI) 在预测组织元素组成和密度方面存在局限性.
- 双能CT (DECT) 提供了更丰富的数据,这可能有助于提高材料的特性.
研究的目的:
- 引入和评估一种新的材料索引方法,即基础向量模型材料索引 (BVM-MI),用于基于DECT的MC剂量规划.
- 为了比较BVM-MI与传统HU-MI方法在预测组织特性和剂量沉积方面的性能.
- 评估BVM-MI对MC模拟中质子剂量计算准确性的影响.
主要方法:
- 开发了BVM-MI,使用DECT的基向量模型权重的多重线性回归来预测70种组织的元素组成和质量密度.
- 将预测的组织特性集成到TOPAS MC代码中,用于质子剂量沉积模拟.
- 使用包括元素组成错误,质量密度错误,平均激发能量,R80深度错误和侧向剂量配置精度在内的指标,对HU-MI进行了BVM-MI的评估.
主要成果:
- 与HU-MI相比,BVM-MI在预测元素组成方面表现出更高的准确性 (例如,软组织的平均RMSE为1.30%,HU-MI为4.20%).
- 在预测R80深度时,BVM-MI显著降低了错误 (例如,软组织的RMSE为0.2mm,HU-MI为1.8mm).
- 侧向剂量配置分析显示,在不同地区,BVM-MI的剂量误差较小.
结论:
- 基向量模型材料索引 (BVM-MI) 在DECT中为材料表征提供了比传统HU-MI的显著改进.
- 在MC模拟中,BVM-MI的精确度提高导致了更精确的质子剂量计算.
- BVM-MI显示了优化质子疗法规划和改善治疗结果的巨大潜力.
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