Toscano Juan Diego, Guo Yisen, Wang Zhibo, Vaezi Mohammad, Mori Yuki, Karniadakis George Em, Boster Kimberly A S, Kelley Douglas H
bioRxiv. 2025 Aug 1:2025.07.30.667741. doi: 10.1101/2025.07.30.667741.
The circulation of cerebrospinal and interstitial fluid plays a vital role in clearing metabolic waste from the brain, and its disruption has been linked to neurological disorders. However, directly measuring brain-wide fluid transport-especially in the deep brain-has remained elusive. Here, we introduce magnetic resonance artificial intelligence velocimetry (MR-AIV), a framework featuring a specialized physics-informed architecture and optimization method that reconstructs three-dimensional fluid velocity fields from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MR-AIV unveils brain-wide velocity maps while providing estimates of tissue permeability and pressure fields-quantities inaccessible to other methods. Applied to the brain, MR-AIV reveals a functional landscape of interstitial and perivascular flow, quantitatively distinguishing slow diffusion-driven transport (∼ 0.1 µm/s) from rapid advective flow (∼ 3 µm/s). This approach enables new investigations into brain clearance mechanisms and fluid dynamics in health and disease, with broad potential applications to other porous media systems, from geophysics to tissue mechanics.
脑脊液和间质液的循环在清除大脑代谢废物方面起着至关重要的作用，其紊乱与神经疾病有关。然而，直接测量全脑范围的液体运输，尤其是深部脑区的液体运输，仍然难以实现。在此，我们介绍磁共振人工智能测速技术（MR-AIV），这是一个具有专门的物理信息架构和优化方法的框架，可从动态对比增强磁共振成像（DCE-MRI）重建三维流体速度场。MR-AIV揭示了全脑速度图，同时提供了组织渗透率和压力场的估计值，而这些量是其他方法无法获得的。应用于大脑时，MR-AIV揭示了间质和血管周围流动的功能景观，定量区分了缓慢扩散驱动的运输（约0.1 µm/s）和快速平流（约3 µm/s）。这种方法能够对健康和疾病状态下的脑清除机制和流体动力学进行新的研究，并在从地球物理学到组织力学等其他多孔介质系统中具有广泛的潜在应用。
