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Magnetic resonance imaging (MRI) is the preferred modality to assess hemodynamics in healthy and diseased blood vessels. As an affordable and non-invasive alternative, Color-Doppler imaging is a good candidate. Nevertheless, Color-Doppler acquisitions provide only partial information on the blood velocity within the vessel. We present a framework to reconstruct 2D velocity fields in the aorta. We generated 2D Color-Doppler-like images from patient-specific Computational Fluid Dynamics (CFD) models of abdominal aortas and evaluated the framework's performance. The 2D velocity field reconstruction is based on the minimization of a cost function, in which the reconstructed velocities are constrained to satisfy fluid dynamics principles. The numerical evaluations show that the reconstructed vector flow fields agree with ground-truth velocities, with an average magnitude error of less than 4% and an average angular error of less than 2

Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario.