Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-informed Machine Learning.

PURPOSE: We present an unsupervised deep learning method to perform flow denoising and super-resolution without high resolution labels. We demonstrate the ability of this model to reconstruct 3D stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions.

METHODS: Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Autoencoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier-Stokes equations.

RESULTS: Our experiments achieved mean squared errors in the true flow reconstruction of order 1.0e-4, and root mean squared residuals of order 1.0e-2 for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the pressure field and 0.82 for the wall shear stress magnitude field.

CONCLUSION: By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20x the input resolution.