分流器
口
动脉瘤
医学
数字减影血管造影
血流动力学
多孔介质
模拟
生物医学工程
多孔性
地质学
放射科
材料科学
计算机科学
外科
血管造影
内科学
复合材料
作者
Jinyu Xu,Christof Karmonik,Ying Yu,Nan Lv,Zhaoyue Shi,Jianmin Liu,Qinghai Huang
标识
DOI:10.1016/j.wneu.2022.04.132
摘要
The Tubridge flow diverter (FD) (MicroPort Medical Co. Ltd., Shanghai, China) is a novel device aimed at reconstructing the parent artery and eliminating the aneurysm. Numerical simulations based on virtual FD deployment allow the assessment of the complex nature of aneurismal flow changes before the actual intervention but are demanding on computational resources. Here, we evaluate an alternative strategy of modeling FD effects for the Tubridge system using a porous medium. The goal of this study is to reduce demands on time and complexity of the simulation procedure for applications in clinical research.Ten patient-specific aneurysm models were reconstructed from retrospectively collected diagnostic 3-dimensional digital subtraction angiographic images. Virtual FDs were deployed (SolidWorks, Dassault Systems, Concord, Massachusetts, USA; Meshmixer, Autodesk, San Rafael, California, USA) and corresponding porous medium patches were constructed at the ostium with a research computational fluid dynamics prototype (Siemens Healthineers, Forchheim, Germany). Hemodynamic conditions were simulated in 2 approaches.Hemodynamics inside the aneurysm based on these 2 approaches were compared. Both approaches yielded similar results. Mean wall shear stress and mean pressure of the aneurysmal wall correlated significantly (r = 0.8, r = 1.0, P < 0.05) as did mean velocity and mean pressure at a region inside the aneurysm, at the ostium and at a cross section containing the main vertex (for velocities r = 0.9; for pressures r = 1.0, P < 0.05). The use of porous medium patches reduced the preparation and simulation time together by approximately 50%.Using a porous medium approach yields comparable mean values for hemodynamic alterations compared to direct virtual FD simulations. Additionally, the porous medium approach greatly reduced the modeling complexity and computation time.
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