We compared steady and pulsatile CFD simulations with 4D Flow MRI in a phantom model of arterial stenosis and investigated the hemodynamic features distal to the occlusion. With CFD, we characterized the flow structure to estimate both the magnitude and degree of anisotropy of the turbulence-related indices including vorticity, wall shear stress, helicity, and swirling strength using Large-Eddy Simulation (LES) and Reynolds-Averaged Navier Stocks (RANS) method. The results revealed that the LES approach captures well both the overall and the detailed ow features in comparison to RANS predictions with the standard k-epsilon model when compared with 4D Flow MRI. Furthermore, the Q-criterion was employed to reveal the temporal evolution of the vortex ring which was observed distal to the stenotic narrowing. It was found that both RANS and LES simulations were highly accurate for wall shear stress when validated against 4D Flow MR Imaging.
In this paper we propose a deep learning framework to estimate pressure from 4D flow MRI. Pressure drop is an important parameter to detect and diagnose different cardiovascular diseases. Accurate estimation of pressure from 4D flow MRI is hampered however due to noise and low resolution of 4D flow data. In the proposed method we consider the pressure estimation as a mapping function between velocity to pressure and employ an encoder-decoder based deep network for the mapping. A computational fluid dynamic model was designed which identically matched the geometry of a stenotic flow phantom used in 4D Flow MRI experiments and velocity and pressure data was simulated for 1000 different flow conditions to train the network. In addition, the proposed network was tested on real in -vitro 4D flow MRI in the same stenotic model for 3 different flow rates. Estimated pressures from the network showed excellent agreement with the reference CFD simulated pressures. As measure of fidelity, relative pressure drop across the stenosis was computed between the reference pressure and estimated pressure and were compared with the simplified Bernouli method. It was determined that the pressure drop estimation by the proposed method is more accurate than competing method.
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