基于深度学习的目标跟踪与X射线图像用于放射治疗:一个叙事审查
Xi Liu1,2,3, Li-Sheng Geng1,4,5, David Huang5,6
1School of Physics, Beihang University, Beijing, China.
Quantitative imaging in medicine and surgery
|March 28, 2024
概括
深度学习表明,使用二维X射线图像进行放射治疗 (RT) 中的无标记标记目标跟踪具有前景. 虽然实时运动管理是可行的,但对于临床应用的精确瘤定位仍然存在挑战.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 辐射瘤学 辐射瘤学
背景情况:
- 放射治疗 (RT) 是一种主要的癌症治疗方式.
- 准确地定位瘤和有风险的器官 (OARs) 对于有效的RT至关重要.
- 来自LINAC的2D kVX射线图像为没有标记器的跟踪提供了潜力,但在软组织对比度和器官重叠方面面临挑战.
研究的目的:
- 审查目前基于深度学习的目标跟踪的发展,使用RT中的2D kVX射线图像.
- 讨论现有的局限性,挑战,潜在的解决方案,以及该领域的未来方向.
主要方法:
- 进行了对英语文章的叙事审查.
- 在 Web of Science,PubMed 和 Google Scholar 上进行了搜索.
- 关键词包括放射治疗,运动跟踪,X射线图像和深度学习;包括23篇文章 (2019年3月至2023年12月).
主要成果:
- 深度学习方法可用于RT中的无标记目标跟踪和实时运动管理.
- 在使用2D kVX射线图像实时定位瘤和OAR方面仍然存在挑战.
- 需要进一步的技术和临床进展.
结论:
- 基于深度学习的2D kVX射线图像目标跟踪是RT运动管理的一个有希望的方法.
- 潜在的好处包括实时运动识别,减少边缘,以及更好地节省正常组织.
- 在广泛的临床采用之前,仍然需要进行重大开发.
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