一个基于机器学习的模型用于剂量点内核计算
Ignacio Scarinci1,2, Mauro Valente3,4,5, Pedro Pérez1,2
1Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, 5000, Córdoba, Argentina.
一个新的机器学习模型准确地预测了用于核医学剂量计的剂量点内核 (DPK). 这种方法可以更快,可靠的患者特定的吸收剂量计算,如放射性栓塞治疗.
科学领域:
- 医学物理 医学物理
- 核医学就是核医学.
- 计算科学 计算科学
背景情况:
- 精确的吸收剂量计算对于有效的核医学治疗至关重要.
- 剂量点核 (DPK) 对于基于卷积的吸收剂量计算至关重要.
- 目前用于DPK生成的方法可能是计算密集的.
研究的目的:
- 开发和实施多目标回归方法,用于为单一能源产生DPK.
- 创建用于核医学中的β发射器获得DPK的模型.
- 为了验证模型在患者特定剂量测量中的准确性.
主要方法:
- 使用FLUKA蒙特卡洛 (MC) 代码计算单能电子源的DPK.
- 使用的回归链 (RC) 具有规范化/收缩模型.
- 评估的β发射器将DPK (sDPK) 与参考数据进行缩放,并将其应用于患者特定的Voxel剂量内核 (VDK) 计算.
主要成果:
- 机器学习模型准确地预测了单能和β发射器的sDPK,平均平均百分比误差低于[公式:参见文本].
- 与完整的MC模拟相比,针对患者的剂量测量计算显示了以下差异[公式:参见文本].
- 开发的模型显示了对预测多种材料和能量DPK的有希望的能力.
结论:
- 一个ML模型已成功开发用于核医学剂量计计算.
- 该模型准确地预测了beta发射放射性核素的sDPKs,使得可靠的患者特异性吸收剂量分布成为可能.
- 该方法显著减少了剂量计计算时间,提高了临床适用性.
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