Keywords: Flow, Velocity & Flow
We propose physics-informed neural networks to augment 2D phase-contrast flow measurements in order to reconstruct full 3D velocity and pressure fields. In this conceptual work, 2D flow measurements are assimilated using the incompressible Navier-Stokes equations defined on the vessel anatomy reconstructed from anatomical scans. The network leverages a low-rank representation of velocity and pressure fields as physical prior and it is demonstrated to reconstruct 3D velocity and pressure gradient fields with good accuracy.
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