图像2Flow:一个概念验证混合图像和图形卷积神经网络,用于快速针对患者的肺动脉细分和CFD流场计算,使用3D心脏MRI数据进行计算
Tina Yao1, Endrit Pajaziti1, Michael Quail1
1Institute of Cardiovascular Science, University College London, London, United Kingdom.
PLoS computational biology
|June 20, 2024
概括
一个新的深度学习模型,Image2Flow,自动化肺动脉细分和计算流体动力学 (CFD) 估计从心脏MRI. 这显著加速了血液动力学分析,使其在临床上可行.
科学领域:
- 医疗成像医学成像
- 计算流体动力学的流体动力学.
- 人工智能的人工智能
背景情况:
- 计算流体动力学 (CFD) 提供了非侵入性的血液动力学评估,但因手动细分和漫长的模拟而受到阻碍.
- 自动化这些过程对于临床适用性至关重要.
研究的目的:
- 开发和验证一个深度学习模型,Image2Flow,用于同时针对患者的肺动脉细分和CFD流场估计3D心脏MRI.
- 显著减少血液动力学分析所需的时间.
主要方法:
- 一个混合图像和图形卷积神经网络 (Image2Flow) 在135个3D心脏MRI上受过训练.
- 该模型将模板转换为患者特定的解剖学,并估计CFD值,使用输入速度作为输入.
- 评估包括细分精度 (Dice分数) 和CFD预测精度 (节点智能比较).
主要成果:
- 图像2流实现了优异的细分精度 (中位数:0.91).
- 精确的CFD估计,中位节点标准化的绝对误差为压力11.75%,速度大小9.90%.
- 该模型展示了对不同进口速度的适当反应.
结论:
- Image2Flow 允许同时对患者进行特定细分和CFD估计,大大减少分析时间 (~330ms 与手动方法相比).
- 这种深度学习方法显著提高了非侵入性血液动力学评估的临床可行性.
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