Data for: On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI
收藏DataONE2022-12-09 更新2025-08-02 收录
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A long-time exposure to lack of oxygen (hypoxia) in some regions of the cerebrovascular system is believed to be one of the causes of cerebral neurological disease. Performing {\em in vivo} studies on the human brain is complicated and can be highly risky for patients. In the present study, we show how a combination of Magnetic Resonance Imaging (MRI) and Computational Fluid Dynamics (CFD) can provide a non-invasive alternative for studying blood flow and transport of oxygen within the cerebral vasculature. We perform computer simulations of oxygen mass transfer in the subject-specific geometry of the Circle of Willis. The computational domain and boundary conditions are based on 4D flow MRI measurements. Two different oxygen mass transfer models are considered: passive (where oxygen is treated as a dilute chemical species in plasma) and active (where oxygen is bonded to hemoglobin) models. We show that neglecting hemoglobin transport results in a significant underestimation of the arte..., The results obtained are performed by the computational fluid dynamics (CFD) of the blood flow in the subject-specific of the brain vascular system.Â
The data are saved in the Tecplot visualization software format (.plt)., The data are saved in the Tecplot visualization software format (.plt) (www.tecplot.com).
The data can be opened directly from Tecplot or by using open-source visualization software ParaView.
脑血管系统部分区域长期暴露于缺氧(hypoxia)状态,被认为是脑血管神经疾病的诱因之一。针对人类大脑开展体内(in vivo)研究不仅操作复杂,还会给患者带来较高风险。本研究展示了磁共振成像(Magnetic Resonance Imaging, MRI)与计算流体动力学(Computational Fluid Dynamics, CFD)联用的非侵入性方案,可用于研究脑血管系统内的血液流动与氧气输运过程。我们针对威利斯环(Circle of Willis)的个体特异性几何结构开展氧气传质的计算机模拟,计算域与边界条件均基于4D流动MRI测量结果构建。本研究考虑了两种不同的氧气传质模型:被动模型(将氧气视为血浆中的稀释化学物质)与主动模型(氧气与血红蛋白结合)。研究表明,忽略血红蛋白输运会显著低估arte...。本研究所得结果基于脑血管系统个体特异性血液流动的计算流体动力学(CFD)模拟完成。数据集以泰克plot可视化软件(Tecplot visualization software)格式(.plt)存储,相关软件信息可参考官网www.tecplot.com。该数据集可直接通过泰克plot可视化软件或开源可视化软件帕拉维(ParaView)打开。
创建时间:
2025-07-17



