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深度学习运动纠正定量压力 perfusion 心血管磁共振
Noortje I P Schueler1, Nathan C K Wong2, Richard J Crawley2
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
概括
一种新的深度学习方法显著加快了心血管磁共振 (CMR) 成像的运动校正. 这种先进的技术提高了定量应力 perfusion CMR 的准确性,使其更有效率地用于临床使用.
科学领域:
- 心血管成像 - 心血管成像
- 医学图像分析 医学图像分析
- 人工智能在医学中的应用
背景情况:
- 定量压力 perfusion 心血管磁共振 (CMR) 对于评估心肌缺血至关重要.
- 运动校正对于高分辨率CMR中准确的自动定量化至关重要.
- 现有的基于注册的方法是计算密集型的,对获取变化敏感.
研究的目的:
- 开发和评估基于深度学习的无监督管道,用于应力 perfusion CMR 中的运动校正.
- 用高效的深度学习方法取代计算上昂贵的代注册.
- 为了提高定量 perfusion CMR 运动校正的强度和速度.
主要方法:
- 开发了一个无监督的深度学习管道,使用主要组件分析来进行动态对比度减轻.
- 集成 perfusion 系列与辅助图像 (动脉输入功能,质子密度加权图像).
- 在多供应商数据 (201名患者) 上训练并验证的模型,在38名患者上进行独立测试.
主要成果:
- 深度学习显著改善了时间强度曲线的时间平滑性 (p<0.001).
- 与基线方法相比,达到了可比的心肌平衡 (Dice=0.92),与未经纠正的图像 (Dice=0.80) 相比显著改善.
- 与代方法相比,处理时间缩短了15倍,具有更流的定量 perfusion 地图.
结论:
- 深度学习方法为压力 perfusion CMR.提供更快,更强大的运动校正.
- 它提高了动态对比度增强和辅助图像系列的精度.
- 它的多供应商培训促进了定量 perfusion CMR 的更广泛的临床采用.
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