侦察剂-TCM:从管道电流调节CT检查中直接和潜在的基于侦察的个性化器官和有效剂量的估计
Maria Jose Medrano1, Sen Wang1, Liyan Sun1
1Department of Radiology, Stanford University, Stanford, CA 94305, United States of America.
Physics in medicine and biology
|November 19, 2025
概括
新的深度学习模型Scout-Dose-TCM在CT扫描中准确估计器官和有效辐射剂量,优于现有的方法,以提高患者安全.
科学领域:
- 医疗成像医学成像
- 辐射物理学 辐射物理学
- 人工智能在医学中的应用
背景情况:
- 在计算机断层扫描 (CT) 中精确估计器官和有效辐射剂量对于患者的安全至关重要.
- 管电流调制 (TCM) 被广泛用于优化辐射剂量,但使剂量估计复杂化.
- 像全球CTDIvol和器官CTDIvol这样的现有方法在准确性上有局限性,特别是TCM.
研究的目的:
- 引入Scout-Dose-TCM,这是一个新的深度学习模型,用于在TCM下直接,前性估计器官水平和有效剂量.
- 为了比较Scout-Dose-TCM与已建立的剂量估计方法 (全球CTDIvol,器官CTDIvol,AAPM报告96) 的性能.
主要方法:
- 开发了Scout-Dose-TCM,这是一个深度学习模型,利用侦察图像和扫描范围进行基于DCT的实时剂量预测.
- 使用蒙特卡罗 (MC) -GPU和TotalSegmentator衍生器官剂量从130个对比度增强的胸部,腹部和骨盆CT检查中训练模型.
- 斯考特剂量-TCM与全球CTDIvol,器官CTDIvol和AAPM报告96方法进行了比较,使用五倍交叉验证和剂量错误.
主要成果:
- 侦察剂量-TCM的平均器官水平绝对百分比误差为7%,明显低于全球CTDIvol (13%) 和器官CTDIvol (9%).
- 该模型在肝脏,膀和胰腺剂量预测方面表现出优异的准确性,与器官CTDIvol.相比.
- 与全球CTDIvol (3.87%),器官CTDIvol (4.16%) 和AAPM报告96 (31%) 相比,侦察剂量-TCM产生了最低的有效剂量误差 (3.01%).
结论:
- 侦察剂量-TCM在TCM下在CT中提供准确的潜在器官和有效剂量估计,优于既有方法.
- 该模型消除了在推断过程中对图像细分的需求,提高了其临床适用性.
- 侦察剂量-TCM在临床CT实践中显示出改善辐射剂量管理的巨大潜力.
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