推断in vivo小鼠脑脊髓液流动使用人工智能速度测量与移动边界和不确定性量化
Juan Diego Toscano1, Chenxi Wu2, Antonio Ladrón-de-Guevara3
1Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.
Interface focus
|December 9, 2024
概括
人工智能速度测量 (AIV) 通过重建3D速度和量化流速来增强脑脊液 (CSF) 流量分析. 这种人工智能驱动的方法提高了理解大脑废物清除和神经退行性疾病的准确性.
科学领域:
- 神经科学是一个神经科学.
- 流体动力学 流体动力学
- 生物医学工程 生物医学工程
背景情况:
- 大脑脊髓液 (CSF) 流动对于大脑的废物清理至关重要,其破坏与阿尔茨海默氏症等神经退行性疾病有关.
- 像粒子跟踪速度测量 (PTV) 这样的传统方法对复杂的3DCSF动态提供了有限的2D洞察力.
研究的目的:
- 开发一种先进的人工智能速度测量 (AIV) 方法,用于精确的3D重建和数量化脑脊液流.
- 为实验数据和模型衍生不确定性实施强大的不确定性量化 (UQ).
主要方法:
- 利用AIV重建3D速度,推断压力,并从实验数据计算墙壁剪切应力.
- 修改了AIV架构,以解决来自噪音数据的 aleatoric 不确定性.
- 实施UQ用于与管理方程和网络表示相关的认识不确定性,测试各种模型和初始化.
主要成果:
- 通过3D速度重建实现了CSF流量量化的精度提升.
- 成功量化了压力和墙壁剪切应力,为流动动力学提供了更深入的见解.
- 在基于物理的机器学习中展示了一个强大的不确定性量化框架.
结论:
- 开发的AIV方法显著提高了CSF流量测量的准确性.
- 这种方法为流体动力学和生物医学研究中的反向问题提供了一种多功能工具.
- 该UQ框架提高了人工智能驱动的生物系统分析的可靠性.
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