Keywords: Phantoms, Heart, Hemodynamics, Flow, Neural network, Deep learningVariations in velocity derivative fluctuations have been correlated with changes in pressure gradient. Image denoising and super-resolution techniques are required to accurately quantify velocity fluctuations. We propose a novel network, which we call TKE-Net to estimate Turbulent Kinetic Energy (TKE) which utilizes a ResNet convolutional neural network backbone. The network is trained and tested with low-resolution simulated CFD data, as could be derived from low resolution 4D flow in a phantom model of arterial stenosis. The results indicate good accuracy in estimating TKE. The method was also applied to in-vitro 4D flow MRI data in identical geometry.
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