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可变形肺部4DCT图像记录通过地标驱动的循环网络.

Luke Matkovic1, Yang Lei1, Yabo Fu2

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.

Medical physics
|September 14, 2023
PubMed
概括

一种新的深度学习方法,标志性驱动循环网络,实现了精确的肺4DCT可变形图像记录. 这种自动化方法改善了呼吸运动量化,以更好地治疗患者.

关键词:
深度学习是一种深度学习.可变形图像的注册 变形图像的注册肺部CT 肺部CT 肺部CT 肺部CT辐射疗法 辐射疗法

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科学领域:

  • 医疗成像医学成像
  • 放射学 放射学是一门学科.
  • 人工智能的人工智能

背景情况:

  • 精确的肺部4DCT图像记录对于量化呼吸运动和优化治疗管理至关重要.
  • 现有的方法需要提高自动化,准确性和效率.

研究的目的:

  • 为4DCT肺部可变形图像注册 (DIR) 开发一种低监督的深度学习方法.
  • 引入一个新的地标驱动的循环网络,以增强DIR.

主要方法:

  • 一个发电机-区分器网络执行DIR,输出变形向量场 (DVF).
  • 发电机是双向优化,地标通过地标驱动的损失提供弱监督.
  • 区分器通过评估变形CT的现实性来规范DVF.

主要成果:

  • 拟议的方法在DIR-Lab数据集上优于现有的深度学习方法,达到1.20 ± 0.72毫米的平均TRE.
  • 双向和地标驱动的损失有效地提高了登记准确性.
  • 临床数据集显示出有希望的结果,MAE为32.1 ± 11.6 HU,SSIM为0.979 ± 0.011.

结论:

  • 标志性驱动循环网络是自动肺部4DCT可变形图像记录的经过验证和有效工具.
  • 该方法表现出与当前最先进的技术相当或超过的性能.