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基于心脏计算机断层扫描的自动3D左心房应变提取框架.

Ling Chen1, Sung-Hao Huang2, Tzu-Hsiang Wang1

  • 1Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Computer methods and programs in biomedicine
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概括

这项研究引入了使用心脏CT进行3D左心房应变分析的自动化深度学习框架,为评估心脏功能提供了回声心脏谱的新替代方案.

关键词:
心脏计算机断层扫描是指心脏的计算机断层扫描.在 LA 细分上,有 LA 的细分.拉斯维加斯拉斯拉斯提取菌株亚临床心房动是一种心房动.

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科学领域:

  • 心脏病学 心脏病学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 左心房 (LA) 应变分析对于心肌功能评估至关重要,但由于解剖学约束,心声学具有局限性.
  • 心脏计算机断层扫描 (CT) 为深入的LA功能评估提供了卓越的空间分辨率.
  • 以前基于CT的菌株研究依赖于手动方法,主要集中在左心室 (LV).

研究的目的:

  • 开发一个完全自动的深度学习 (DL) 框架,用于从心脏CT中提取3D LA菌株.
  • 克服手动ROI选择和心声回声学在LA菌株分析中的局限性.
  • 通过使用心脏CT,使LA功能的全面3D评估成为可能.

主要方法:

  • 使用2.5DGN-U-Net开发了一个3D菌株提取框架,用于心脏CT上的LA细分.
  • 集成的轴向3D视图提取和LA应变测量.
  • 验证了细分精度 (DSC),并将LA体积/EF与地面真相 (ICC,r,B-A图) 进行了比较.
  • 评估自动提取的LA菌株与2D心声回声学测量进行对比.
  • 评估了心房 (AF) 负担对LA菌株的影响,使用心房高频发作 (AHRE) 负担.

主要成果:

  • 该GN-U-Net实现了高的LA细分精度 (DSC=0.9603).
  • 框架中提取的LA体积和EF显示与专家测量 (ICCs>0.902) 有很好的一致性.
  • 在LA菌株中,与二维心声学 (ICCs>0.703) 显示了中等一致性.
  • 在具有较高AHRE负担 (> 6分钟) 的患者中观察到明显较低的全球菌株和LAEF.

结论:

  • 基于DL的框架使得自动和可行的3DLA可以从心脏CT中提取应变.
  • 这种自动化工具为评估LA功能提供了一个有希望的3D心声回声学替代方案.
  • 该框架能够将AF负担与AF负担相关联,这突显了其临床实用性.