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Magnetically-Assisted Remote Controlled Microcatheter Tip Deflection under Magnetic Resonance Imaging
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基于深度学习的潜在切片跟踪用于在MRI导向心脏导管治疗期间的连续导管可视化.

Alexander Paul Neofytou1, Grzegorz Kowalik1, Rohini Vidya Shankar1

  • 1School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.

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概括

一种新的人工智能 (AI) 深度学习技术在MR导向心脏导管过程中实现了自动无参数导管跟踪. 这种基于人工智能的方法在幻影和患者研究中表现出高准确性和可行性.

关键词:
关于MR的指导意见心脏导管治疗的心脏导管治疗深度学习是一种深度学习.被动跟踪是一种被动的跟踪.实时实时的时间.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 心血管干预 心血管干预

背景情况:

  • 用MR指导的心脏导管治疗需要精确的导管跟踪才能准确地导航.
  • 目前的跟踪方法可能取决于操作员,需要手动调整参数.
  • 存在对自动化,实时导管追踪解决方案的需求.

研究的目的:

  • 介绍一种新的,基于深度学习的,无参数的,自动切片跟踪技术.
  • 为了在MRI导向的心脏导管过程中实现持续的导管跟踪和可视化.
  • 评估这种基于人工智能的方法的可行性和性能.

主要方法:

  • 采用了带有ResNet-34编码器的U-Net架构用于导管尖端识别.
  • 该技术利用校准和运行时模式进行初始定位和动态跟踪.
  • 前性评估在一个心脏幻影和3名患者中进行,与非AI方法进行了回顾性比较.

主要成果:

  • 人工智能跟踪框架在幻影研究中实现了100%的准确性,灵敏性和特异性.
  • 在患者研究中,平均准确度/灵敏度/特异性为100%/100%/100% (校准) 和98.4%/94.1%/100% (运行时间).
  • 人工智能方法的性能与以前的非人工智能跟踪技术相比或更高.

结论:

  • 一个基于人工智能的前景切片跟踪框架已成功开发.
  • 该技术提供实时,无参数,独立于操作者的导管跟踪.
  • 在接受MRI导向心脏导管治疗的患者中证明了可行性.