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一种基于机器学习的双能CT元素分解方法及其对碳离子疗法的物理生物学影响

Yan Li^1,2, Weiguang Li^1,2,3, Chao Yang²

¹School of Physics, Beihang University, Beijing, China.

Medical physics

|September 10, 2025

概括

一种基于机器学习的新双能计算断层扫描 (ML-DECT) 方法准确地估计了改善碳离子治疗的组织组成. 与传统技术相比,这种方法显示出更高的准确性和噪声强度.

科学领域:

医疗成像医学成像
放射治疗物理物理
计算生物学计算生物学

背景情况:

双能计算断层扫描 (DECT) 利用能量依赖衰减来实现材料差异化.
准确的组织元素组成估计对于在碳离子治疗中量化物理和生物剂量至关重要.

研究的目的:

开发一种基于机器学习的DECT (ML-DECT) 方法,用于预测物理密度和元素质量比 (H,C,N,O,P,Ca).
研究ML-DECT对碳离子治疗结果的物理和生物影响.

主要方法:

一个完全连接的神经网络利用DECT衍生的CT数字来预测物理密度和元素质量比.
数据集包括85种生物组织与数据增强;预测与参数化DECT (PA-DECT) 和SECT方法进行了比较.
与PA-DECT相比,ML-DECT应用于10名头患者的DECT图像,用于蒙特卡洛模拟的物理/生物剂量和线性能量转移 (LET) 计算,与PA-DECT相比.

主要成果:

在物理密度方面,ML-DECT实现了R2=0.9996和元素质量比的R2=0.83380.9997.
精度提高了>20% (PA-DECT) 和>50% (SECT);噪声强度提高了>3倍 (PA-DECT) 和高达25% (SECT).
患者剂量评估显示了可比的物理/生物剂量,~1%高的LET,和~2mm浅的布拉格峰值与ML-DECT对比PA-DECT.

结论:

ML-DECT能够精确估计人体组织的物理密度和元素质量比.
与PA-DECT方法相比,ML-DECT在图像噪声方面表现出更高的稳定性,提高了其临床适用性.

关键词:

碳离子疗法是一种碳离子疗法.分解,分解,分解.双能量CT是双能量CT.基本的分解元素分解.机器学习是机器学习.物理-生物影响.

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