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深度学习用于放射治疗中的CT合成

Yike Guo¹, Yi Luo¹, Hamed Hooshangnejad^1,2

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA.

Bioengineering (Basel, Switzerland)

|December 30, 2025

概括

人工智能 (AI) 深度学习 (DL) 方法正在通过生成合成CT (sCT) 图像来彻底改变放射瘤学. 这项技术增强了图像引导的自适应性放射治疗和无模拟的工作流程,改善了患者的护理.

科学领域:

辐射疗法辐射疗法
医疗成像医学成像
人工智能的人工智能

背景情况:

深度学习 (DL) 方法越来越多地被整合到辐射瘤学中.
合成计算机断层扫描 (sCT) 图像生成是一个关键的兴趣领域.
sCT支持先进的临床场景,如图像引导适应性放射治疗 (IGART) 和无模拟工作流.

研究的目的:

在放射治疗中提供基于DL的sCT合成的全面审查.
讨论各种成像模式的临床应用.
检查DL模型架构和培训策略.

主要方法:

关于基于DL的sCT合成的最近研究的综述.
对卷积神经网络 (CNN) 和生成对抗网络 (GAN) 的分析.
检查多模式成像输入 (CBCT,MRI,诊断CT).

主要成果:

基于DL的sCT合成在多个临床应用中显示出显著的潜力.
包括CNN和GAN在内的各种DL架构对于sCT生成是有效的.
新兴的培训策略正在提高sCT的质量和临床适用性.

结论:

人工智能驱动的sCT生成可以减少成像负担,并提高放射治疗中的剂量准确性.
通过sCT加速工作流效率有潜力显著改善患者治疗结果.
需要进一步的研究来解决DL算法的临床翻译方面的挑战.

关键词:

深度学习 (DL) 是指深度学习.辐射疗法辐射疗法合成CT (sCT) 的使用.

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