用卷积神经网络对F-flurpiridaz PET-MPI进行动态对运动校正
Meghana Urs1, Aditya Killekar1, Valerie Builoff1
1Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Los Angeles, CA, 90048, USA.
European journal of nuclear medicine and molecular imaging
|November 19, 2025
概括
深度学习运动校正 (DL-MC) 为手动方法提供了一个更快,更具诊断效果的替代方法,用于18F-flurpiridaz PET心肌血流量 (MBF) 和流量储备 (MFR) 的量化. 这种自动化方法与专家手动校正取得了很好的一致性,提高了心脏PET成像的效率.
科学领域:
- 核医学是一种核医学.
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
背景情况:
- 使用F-flurpiridaz PET精确量化心肌血流量 (MBF) 和心肌流量储备 (MFR),对于诊断冠状动脉疾病 (CAD) 至关重要.
- 在PET成像中的运动工件显著影响MBF和MFR量化的精度.
- 目前的手动运动校正 (MC) 方法耗时,依赖操作员,需要广泛的专业知识,导致观察者之间的变化.
研究的目的:
- 在F-flurpiridaz PET成像中开发和验证用于自动化运动校正 (MC) 的深度学习 (DL) 框架.
- 将基于DL的MC的诊断性能和定量一致性与手动MC和标准非AI自动MC方法进行比较.
主要方法:
- 使用3D-ResNet架构从3D PET卷中生成运动向量.
- 该DL框架是使用第三阶段临床试验 (NCT01347710) 的数据进行训练和验证的,经验丰富的操作人员使用手动校正作为地面真相.
- 用模拟运动向量的数据增强增强了DL模型的稳定性. 性能与手动MC和标准非AI自动MC技术进行了评估.
主要成果:
- 检测显著CAD的ROC曲线下的面积 (AUC) 在DL-MC (0.897),手动MC (0.892,0.889) 和优于没有MC (0.835) 之间是可比的.
- DL-MC的诊断准确度与标准的非AI自动MC (AUC0.877) 相当.
- 定量分析显示,DL-MC和手动MC之间的MFR (95%置信限±0.49) 和压力MBF (±0.24毫升/克/分钟) 之间有很好的一致性.
结论:
- 基于深度学习的运动校正 (DL-MC) 为F-flurpiridaz PET提供了比手动MC更快的替代方案.
- 在评估显著的CAD方面,DL-MC实现了与手动MC可比的诊断性能,并且在评估显著的CAD方面优于没有MC.
- 来自DL-MC的定量MBF和MFR结果与专家手动校正显示出很好的一致性,使其成为PET-MPI的可靠工具.
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