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The reconstruction of blood velocity in a vessel from contrast enhanced x‐ray computed tomography projections is a complex inverse problem. It can be formulated as reconstruction problem with a partial differential equation constraint. A solution can be estimated with the a variational adjoint method and proper orthogonal decomposition (POD) basis. In this work, we investigate new inversion approaches based on PODs coupled with deep learning methods. The effectiveness of the reconstruction methods is shown with simulated realistic stationary blood flows in a vessel. The methods outperform the reduced adjoint method and show large speed‐up at the online stage. [ABSTRACT FROM AUTHOR]

In this study, a coupled dimensionally-heterogeneous model of the systemic circulation is applied in order to study computationally the influence of the heart rate in the local hemodynamics. This is achieved by using a 3D-1D-0D model of the whole arterial tree accounting for: details of the local blood flow in the vessel of interest through 3D models, the wave propagation phenomena in the larger arteries by means of 1D models and, the peripheral microvascular beds via 0D models. Two applications are presented which aim at assessing the influence of the global setting of the vascular network on the local hemodynamics. In the first example, the hemodynamics in a patient-specific model of the abdominal aorta is studied for two different systemic scenarios corresponding to different resting heart rates. In the second example, the blood flow in a standardized model of the carotid artery is analyzed for different heart rates including the resting state and several other exercise regimes. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]