On the anatomical definition of arterial networks in blood flow simulations: comparison of detailed and simplified models.

The goal of this work is to assess the impact of vascular anatomy definition degree in the predictions of blood flow models of the arterial network. To this end, results obtained with an anatomically detailed network containing over 2000 vessels are systematically compared with those obtained with an anatomically simplified network containing the main 86 vessels, the latter being a truncated version of the former one. The comparison is performed quantitatively and qualitatively in terms of pressure and flow rate waveforms, wave intensity analysis and impedance analysis. Comparisons are performed under physiological conditions and for the case of common carotid artery occlusion. Mechanisms of blood flow delivery to the brain, as well as different blood flow steal phenomena, are unveiled in light of model predictions. Results show that detailed and simplified models are in reasonable agreement regarding the hemodynamics in larger vessels and in healthy scenarios. The anatomically detailed arterial network features improved predictive capabilities at peripheral vessels. Moreover, discrepancies between models are substantially accentuated in the case of anatomical variations or abnormal hemodynamic conditions. We conclude that physiologically meaningful agreement between models is obtained for normal hemodynamic conditions. This agreement rapidly deteriorates for abnormal blood flow conditions such as those caused by total arterial occlusion. Differences are even larger when modifications of the vascular anatomy are considered. This rational comparison allows us to gain insight into the need for anatomically detailed arterial networks when addressing complex hemodynamic interactions.

Blood pressure gradients in cerebral arteries: a clue to pathogenesis of cerebral small vessel disease.

RATIONALE: The role of hypertension in cerebral small vessel disease is poorly understood. At the base of the brain (the 'vascular centrencephalon'), short straight arteries transmit blood pressure directly to small resistance vessels; the cerebral convexity is supplied by long arteries with many branches, resulting in a drop in blood pressure. Hypertensive small vessel disease (lipohyalinosis) causes the classically described lacunar infarctions at the base of the brain; however, periventricular white matter intensities (WMIs) seen on MRI and WMI in subcortical areas over the convexity, which are often also called 'lacunes', probably have different aetiologies. OBJECTIVES: We studied pressure gradients from proximal to distal regions of the cerebral vasculature by mathematical modelling. METHODS AND RESULTS: Blood flow/pressure equations were solved in an Anatomically Detailed Arterial Network (ADAN) model, considering a normotensive and a hypertensive case. Model parameters were suitably modified to account for structural changes in arterial vessels in the hypertensive scenario. Computations predict a marked drop in blood pressure from large and medium-sized cerebral vessels to cerebral peripheral beds. When blood pressure in the brachial artery is 192/113 mm Hg, the pressure in the small arterioles of the posterior parietal artery bed would be only 117/68 mm Hg. In the normotensive case, with blood pressure in the brachial artery of 117/75 mm Hg, the pressure in small parietal arterioles would be only 59/38 mm Hg. CONCLUSION: These findings have important implications for understanding small vessel disease. The marked pressure gradient across cerebral arteries should be taken into account when evaluating the pathogenesis of small WMIs on MRI. Hypertensive small vessel disease, affecting the arterioles at the base of the brain should be distinguished from small vessel disease in subcortical regions of the convexity and venous disease in the periventricular white matter.

A high-order local time stepping finite volume solver for one-dimensional blood flow simulations: application to the ADAN model.

In recent years, the complexity of vessel networks for one-dimensional blood flow models has significantly increased, because of enhanced anatomical detail or automatic peripheral vasculature generation, for example. This fact, along with the application of these models in uncertainty quantification and parameter estimation poses the need for extremely efficient numerical solvers. The aim of this work is to present a finite volume solver for one-dimensional blood flow simulations in networks of elastic and viscoelastic vessels, featuring high-order space-time accuracy and local time stepping (LTS). The solver is built on (i) a high-order finite volume type numerical scheme, (ii) a high-order treatment of the numerical solution at internal vertexes of the network, often called junctions, and (iii) an accurate LTS strategy. The accuracy of the proposed methodology is verified by empirical convergence tests. Then, the resulting LTS scheme is applied to arterial networks of increasing complexity and spatial scale heterogeneity, with a number of one-dimensional segments ranging from a few tens up to several thousands and vessel lengths ranging from less than a millimeter up to tens of centimeters, in order to evaluate its computational cost efficiency. The proposed methodology can be extended to any other hyperbolic system for which network applications are relevant. Copyright © 2016 John Wiley & Sons, Ltd.