MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.
Authors
Affiliations (4)
Affiliations (4)
- Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.
- Department of Mechanical Engineering, University of Rochester, Rochester, NY 14627, USA.
- School of Engineering, Brown University, Providence, RI 02912, USA.
- Center for Translational Neuromedicine, University of Copenhagen, Copenhagen 2200, Denmark.
Abstract
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 micrometers per second (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 medium systems, from geophysics to tissue mechanics.