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预测放射治疗中的解剖学变异,使用矢量量化变量自编码器生成模型.

Yue Zou1,2,3, Zhenhao Li1,2,3, Menghan Zhang1,2,3

  • 1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.

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概括
此摘要是机器生成的。

这项研究引入了一种新的生成模型,使用矢量量化变异自编码器 (VQ-VAE) 来预测接受放射治疗的鼻癌患者的解剖变化. 该VQ-VAE模型准确地预测了每天的解剖变化,有助于做出适应性放射治疗决策.

关键词:
适应性放射疗法适应性放射疗法解剖学上的变化.深度学习是一种深度学习.鼻癌 鼻癌 鼻癌

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科学领域:

  • 医疗成像医学成像
  • 辐射疗法 辐射疗法
  • 人工智能的人工智能

背景情况:

  • 放射治疗期间的解剖学变化可以改变辐射输送.
  • 预测这些变化对于有效的适应性放射治疗 (ART) 在鼻癌 (NPC) 中至关重要.

研究的目的:

  • 开发和评估基于矢量量化变异自编码器 (VQ-VAE) 的生成模型,用于预测NPC患者的解剖变化.

主要方法:

  • 拟议的模型将VQ-VAE与自适应实例规范化 (AdaIN) 集成在一起.
  • 一个卷积神经网络 (CNN) 从计划CT图像中提取潜在代码,以捕捉解剖变异.
  • AdaIN调节VQ-VAE潜伏空间,以生成每日CT图像,反映解剖变化.
  • 该模型在90名NPC患者的522张CT图像上进行了训练,并在18名患者的102张CT图像上进行了测试.

主要成果:

  • 与个人患者水平的实际图像相比,生成的每日CT图像在风险器官 (OAR) 体积分布中没有显著差异.
  • 人口水平预测的平均ROI数量与地面真实值密切匹配,并且表现优于之前的每日解剖模型 (DAM).
  • 观察到高的皮尔森相关系数 (0.87-0.93) 实际和生成的每日CTROI体积之间的状腺和下下腺.

结论:

  • 基于计划CT扫描,VQ-VAE模型在预测放射治疗期间的解剖变化方面表现出有效性.
  • 这种预测能力具有很大的潜力,可以为鼻癌放射治疗的适应性决策提供信息.