基于物理学的神经网络用于绘制血管和组织动态图,使用激光光斑对比成像
bioRxiv : the preprint server for biology
|February 12, 2026
概括
一个新的物理信息神经网络 (PINN) 从激光光斑对比成像 (LSCI) 快速量化大脑血流和组织动态. 这种人工智能方法将分析从数小时加速到几秒钟,有助于中风研究.
科学领域:
- 生物医学光学 生物医学光学
- 神经成像是一种神经成像.
- 人工智能在医学中的应用
背景情况:
- 激光光斑对比成像 (LSCI) 对于研究大脑血流,神经血管合和中风至关重要.
- 传统的LSCI分析方法缓慢且难以扩展,阻碍了实时应用.
- 需要有效的基于物理的方法来从LSCI数据中提取血管和组织动态.
研究的目的:
- 开发和验证一个基于物理学的神经网络 (PINN),用于从LSCI进行血管和组织动态的定量估计.
- 为了实现直接估计快速 (血管) 和缓慢 (组织相关) 斑点脱离关系参数,而无需基准真相标签.
- 为了实现全场LSCI测量的快速,像素智能分析.
主要方法:
- 开发了一个PINN,将分析LSCI模型集成到网络的损失函数中,以实现物理一致性.
- 在LSCI图像中采用自主监督学习方法进行像素智能推断.
- 使用实体鼠标中风LSCI数据集验证了PINN框架.
主要成果:
- 该PINN准确地恢复了快速的脱相关率 (大脑血流) 和缓慢的动态 (组织/细胞运动).
- 生成了与传统方法相比的参数图,但实现了数量级更快的分析速度 (秒比小时).
- 证明了对未见的主题的概括性和在噪音条件下的强度.
结论:
- 基于物理学的学习为LSCI的血管和细胞生物标志物近乎实时提取提供了实用框架.
- 这种方法可以有效地纵向监测中风进展.
- 这种方法有可能促进基于LSCI的诊断的临床翻译.
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