TKE-Net: Deep Learning for Estimation of Super-Resolved Turbulent Kinetic Energy Maps from 4D-Flow MRI Data

Arterial stenosis is one of the most prevalent diseases with significant morbidity and mortality requiring accurate quantification of hemodynamic parameters for diagnosis and prognosis. In particular, variations in velocity derivatives have been correlated with variations in pressure gradient, an important marker of hemodynamic significance of stenosis. 4D flow MRI provides time-resolved 3D velocity mapping, however, image denoising and super-resolution techniques are required for precise velocity fluctuation quantification. To address this issue, we propose TKE-Net, a novel network that uses a ResNet convolutional neural network to estimate Turbulent Kinetic Energy (TKE). We trained and tested the network with high-resolution simulated CFD data in a phantom model of arterial stenosis. Our proposed network was further tested on in-vitro 4D flow MRI data in identical geometry, demonstrating good accuracy in estimating TKE.