University of Amsterdam, Informatics Institute, Computational Science Lab, Amsterdam, Netherlands.
Institute of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary.
Adv Neurobiol. 2024;36:397-412. doi: 10.1007/978-3-031-47606-8_20.
Computing the emerging flow in blood vessel sections by means of computational fluid dynamics is an often applied practice in hemodynamics research. One particular area for such investigations is related to the cerebral aneurysms, since their formation, pathogenesis, and the risk of a potential rupture may be flow-related. We present a study on the behavior of small advected particles in cerebral vessel sections in the presence of aneurysmal malformations. These malformations cause strong flow disturbances driving the system toward chaotic behavior. Within these flows, the particle trajectories can form a fractal structure, the properties of which are measurable by quantitative techniques. The measurable quantities are well established chaotic properties, such as the Lyapunov exponent, escape rate, and information dimension. Based on these findings, we propose that chaotic flow within blood vessels in the vicinity of the aneurysm might be relevant for the pathogenesis and development of this malformation.
利用计算流体动力学计算血管截面中的新生流是血液动力学研究中常用的方法。这种研究的一个特定领域与脑动脉瘤有关,因为它们的形成、发病机制和潜在破裂的风险可能与血流有关。我们提出了一项关于在存在脑动脉瘤畸形的情况下小受迫粒子在脑血管段中行为的研究。这些畸形会引起强烈的流动干扰,使系统向混沌行为发展。在这些流动中,粒子轨迹可以形成分形结构,其性质可以通过定量技术来测量。可测量的量是已建立的混沌特性,如 Lyapunov 指数、逃逸率和信息维数。基于这些发现,我们提出,在动脉瘤附近的血管中的混沌流动可能与这种畸形的发病机制和发展有关。