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CT-CBCT可变形注册使用弱监督的文物抑制转移学习网络.

Dingshu Tian1,2, Guangyao Sun3,4, Huaqing Zheng4,5

  • 1University of Science and Technology of China, Hefei 230026, People's Republic of China.

Physics in medicine and biology
|July 11, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的两阶段神经网络,以改进计算机断层扫描-形束计算机断层扫描 (CT-CBCT) 通过抑制CBCT图像中的散射器件来进行适应性放射治疗的可变注册.

关键词:
在CT-CBCT之间.艺术品的抑制,艺术品的抑制.可以变形的注册登记.转移学习网络转移学习网络监管能力较弱的监管机构.

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科学领域:

  • 医疗成像医学成像
  • 放射治疗 物理 物理
  • 人工智能在医学中的应用

背景情况:

  • CT和CBCT的可变形注册对于适应性放射治疗至关重要,使瘤跟踪和器官保护成为可能.
  • 目前的神经网络注册方法对CBCT中的散射工件敏感,导致精度降低.
  • 在CBCT中的工件不一致地影响像素灰色值,导致显著的注册错误.

研究的目的:

  • 为CT-CBCT可变形注册开发一种新的方法,有效地抑制CBCT文物.
  • 为了提高适应性放射治疗应用的注册准确性和可靠性.
  • 利用转移学习和两阶段网络来增强文物处理.

主要方法:

  • 一个直方图分析发现,文物在不感兴趣的区域更突出.
  • 提出了一个监督较弱的,两阶段的转移学习网络.
  • 第一个阶段采用了一个预培训网络,用于在不感兴趣的区域中抑制文物.
  • 第二阶段使用了卷积神经网络来记录人工物抑制的CBCT和CT.

主要成果:

  • 对胸部CT-CBCT数据的比较测试表明,在人工物抑制后,登记准确度显著提高.
  • 拟议的方法优于不包含文物抑制的现有算法.
  • 登记的合理性和准确性得到了验证.

结论:

  • 拟议的多阶段神经网络有效地抑制了CBCT图像中的工件.
  • 预训练技术和注意力机制的整合进一步提高了注册表现.
  • 这种方法为适应性放射治疗中精确的CT-CBCT可变形注册提供了一个有前途的解决方案.