TransVort: A Temporally-Coherent Physics-Guided Neural Network for Super-Resolving and Denoising 4D Flow MRI of Cerebrospinal Fluid.
Authors
Abstract
To enhance the diagnostic utility of 4D flow MRI in assessing cerebrospinal fluid (CSF) dynamics by super-resolving and denoising measured velocities using temporally coherent, physics-guided neural networks (PGNN). Synthetic 4D flow MRI was generated from 40 computational fluid dynamics (CFD) simulations across 10 ventricular geometries. These simulations were used to generate paired synthetic 4D flow MRI and high-resolution velocity fields used for supervised training. Here, we compare a previously developed temporally independent network (div-mDCSRN-Flow) using divergence-based regularization with two novel temporal PGNNs (tempo-mDCSRN-Flow using divergence-regularization and TransVort additionally constrained by the vorticity transport equation). In application to synthetic 4D flow MRI of a double gyre flow showed the temporal PGNNs improve vorticity estimation. Similarly, both temporal methods improved estimation of vorticity and time-averaged wall shear stress (TAWSS) of synthetic 4D flow MRI in the 3rd and 4th ventricle. While using temporal PGNNs improves velocity and vorticity quantification across temporal resolutions, TransVort demonstrated additional improvement at fine temporal resolutions. Application of TransVort to in vivo 4D flow MRI of CSF flow captured vortex formation and dissipation in the 4th ventricle over the cardiac cycle. Leveraging the temporal information of 4D flow MRI improves reconstruction of high-resolution velocity fields. This leads to better estimation of gradient-based flow metrics such as vorticity and TAWSS, which are associated with neurodegenerative and neurovascular diseases. Augmenting the accuracy of 4D flow MRI increases its potential for adoption as a clinical tool for diagnosing and monitoring disorders of neurofluid dynamics.