使用深度学习框架进行三管流量测量,用于自动跟踪2D相位对比度
Jérôme Lamy1,2, Ricardo A Gonzales3, Jie Xiang1
1Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
Magnetic resonance in medicine
|May 31, 2024
概括
这项研究引入了一种使用深度学习的自动化方法来跟踪三管,改善了测量血液流量的测量,这对于评估透气功能至关重要. 这种新技术提高了评估三回速度的准确性,解决了一个关键的临床挑战.
科学领域:
- 心血管成像 - 心血管成像
- 医疗技术 医疗技术 医学技术
- 人工智能在医学中的应用
背景情况:
- 使用心血管磁共振 (CMR) 测量三管的流速是困难的,因为的快速运动.
- 准确评估三管动力学对于评估透气功能至关重要.
- 目前的方法在直接量化通过动态三管的流量方面存在局限性.
研究的目的:
- 开发和验证一种自动化的2D跟踪方法,用于测量通过动态三管的流量.
- 为了克服与移动三管平面中直接流量评估相关的挑战.
- 通过精确的三管流量测量来改善对透缩功能的评估.
主要方法:
- 使用深度学习网络 (TVnet) 来自动从电影图像中跟踪三管平面.
- 使用了一个动态的2D相对比 (PC) 序列,采集平面与跟踪的门运动同步.
- 测量结果与静态2D-PC扫描和来自平面测量和大血管PC成像的腹腔中风体积进行了比较.
主要成果:
- 2D门追踪PC方法与右心室中风体积 (ICC=0.92) 和大动脉PC (ICC=0.87) 有着很好的相关性.
- 与静态2D-PC相比,与右心室中风体积相比,门跟踪方法显示的偏差明显较小 (p=0.01).
- 该技术成功地测量了三管的反速度,包括一个与心声回声相一致的患者的高速喷气.
结论:
- 使用相对比成像的自动 2D 门跟踪是一种可行的方法来评估三管流量和反速度.
- 这种方法为临床挑战提供了一个潜在的解决方案,即准确评估三巴的功能.
- 这种技术显示出增强对扩张功能和膜心脏病的评估的前景.
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