Image-based modeling of the hemodynamics in cerebral arterial trees

Abstract

Knowledge of the hemodynamics in normal arterial trees of the brain is important to better understand the mechanisms responsible for the initiation and progression of cerebrovascular diseases. Information about the baseline values of hemodynamic variables such as velocity magnitudes, swirling flows, wall shear stress, pressure drops, vascular resistances, etc. is important for characterization of the normal hemodynamics and comparison with pathological states such as aneurysms and stenoses. This paper presents image-based computational hemodynamics models of cerebral arterial trees constructed from magnetic resonance angiography (MRA) images. The construction of large models of cerebral arterial trees is challenging because of the following main reasons: a) it is necessary to acquire high resolution angiographic images covering the entire brain, b) it is necessary to construct topologically correct and geometrically accurate watertight models of the vasculature, and c) the models typically result in large computational grids which make the calculations computationally demanding. This paper presents a methodology to model the hemodynamics in the brain arterial network that combines high resolution MRA at 3T, a vector representation of the vascular structures based on semi-manual segmentation, and a novel algorithm to solve the incompressible flow equations efficiently in tubular geometries. These techniques make the study of the hemodynamics in the cerebral arterial network practical.