PhysVENeT:一个基于生理学信息的基于深度学习的框架,用于合成3D超极化气体MRI通风的合成
Joshua R Astley1,2, Alberto M Biancardi2, Helen Marshall2
1Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK.
Scientific reports
|July 12, 2023
概括
我们开发了PhysVENeT,这是一个使用1H-MRI创建3D肺透气替代物的深度学习模型. 这种技术准确地绘制了通风缺陷,为肺部成像提供了高极化气体MRI的有希望的替代方案.
科学领域:
- 医疗成像医学成像
- 肺部医学 肺部医学
- 人工智能的人工智能
背景情况:
- 超极化气体MRI提供功能性肺透气数据,但临床用途有限.
- 质子 (1H) -MRI通风映射显示了与高极化气体MRI的中等相关性.
- 深度学习 (DL) 显示了合成功能性肺部图像的潜力.
研究的目的:
- 开发一种新的3D深度学习框架 (PhysVENeT) 用于合成使用多膨胀结构1H-MRI的3D肺透气替代物.
- 评估与现有方法相比,PhysVENeT在反映通风缺陷方面的准确性.
- 评估PhysVENeT在各种数据集上的表现,包括COVID-19后患者.
主要方法:
- 设计了一个3D多通道卷积神经网络 (PhysVENeT).
- 该网络将生理学信息的通风映射与多膨胀结构的1H-MRI数据集成在一起.
- 从170名参与者中使用了配对的吸入/呼出1H-MRI和高极化气体MRI扫描,在20名COVID-19后患者中进行了外部验证.
主要成果:
- PhysVENeT显著优于传统的1H-MRI通风映射和缺乏结构整合的DL方法.
- 框架准确地反映了通风缺陷.
- 在外部验证数据上观察到最小的过.
结论:
- PhysVENeT提供了一种精确的方法,用于从1H-MRI中合成3D肺透气替代物.
- 这种DL方法为高极化气体MRI提供了一个可行的,可访问的替代方案,用于评估区域肺透气.
- PhysVENeT在不同的肺病理中表现出强大的性能和通用性.
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