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
|May 22, 2024
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.
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.
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