大脑的非侵入性量化[18F]FDG-PET使用推断的血液输入函数从全身数据中学习的物理约束
IEEE transactions on medical imaging
|February 22, 2024
概括
这项研究引入了一种深度学习方法,用于估计大脑PET扫描的输入函数,避免侵入性程序. 开发的深度学习输入函数 (DLIF) 准确量化大脑活动,提高了临床可行性.
科学领域:
- 核医学是一种核医学.
- 医疗成像中的人工智能
背景情况:
- 量化大脑正子发射断层扫描 (PET) 需要血液输入函数 (IF).
- 传统的动脉导管治疗IF是侵入性的,耗时的,并且在临床上是不可行的.
- 开发用于IF估计的非侵入性方法对于常规的大脑PET成像至关重要.
研究的目的:
- 开发和验证一种基于深度学习的方法,用于从动态大脑FDG PET扫描中估计输入函数 (DLIF).
- 为了提高脑PET成像中的运动建模的准确性和可靠性.
- 为了使量化大脑PET的非侵入性,常规的临床应用.
主要方法:
- 采用了结合长短期记忆 (LSTM) 和完全连接网络的深度学习模型.
- 训练数据来自85个全身动态PET扫描,使用来自大脑区域和大脑动脉的时间活动曲线作为输入.
- 动力建模的适合性被纳入为物理损失,以提高准确性并减少训练数据的依赖性.
主要成果:
- 与现有方法相比,DLIF模型生成的输入函数在形状和振幅方面更接近参考值.
- 使用DLIF计算的区域动力参数显示了与参考值的高相关性 (0.961,0.913) 和低偏差 (1.68±8.74%,0.37±4.93%).
- 使用DLIF获得的参数图像在视觉和定量上与参考图像非常相似.
结论:
- 训练有素的深度学习模型可以准确地从动态大脑PET数据中推断出图像衍生的输入函数.
- DLIF促进了可靠的动力学建模,为动脉采样提供了一个非侵入性的替代方案.
- 这种方法提高了定量脑PET成像的临床实用性和可访问性.
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