Department of Mechanical Engineering, University of Washington, Seattle, WA 98195.
Department of Neurological Surgery, University of Washington, Seattle, WA 98195; Stroke and Applied Neuroscience Center, University of Washington, Seattle, WA 98195.
J Biomech Eng. 2021 Jul 1;143(7). doi: 10.1115/1.4050375.
As frequency of endovascular treatments for intracranial aneurysms increases, there is a growing need to understand the mechanisms for coil embolization failure. Computational fluid dynamics (CFD) modeling often simplifies modeling the endovascular coils as a homogeneous porous medium (PM), and focuses on the vascular wall endothelium, not considering the biomechanical environment of platelets. These assumptions limit the accuracy of computations for treatment predictions. We present a rigorous analysis using X-ray microtomographic imaging of the coils and a combination of Lagrangian (platelet) and Eulerian (endothelium) metrics. Four patient-specific, anatomically accurate in vitro flow phantoms of aneurysms are treated with the same patient-specific endovascular coils. Synchrotron tomography scans of the coil mass morphology are obtained. Aneurysmal hemodynamics are computationally simulated before and after coiling, using patient-specific velocity/pressure measurements. For each patient, we analyze the trajectories of thousands of platelets during several cardiac cycles, and calculate residence times (RTs) and shear exposure, relevant to thrombus formation. We quantify the inconsistencies of the PM approach, comparing them with coil-resolved (CR) simulations, showing the under- or overestimation of key hemodynamic metrics used to predict treatment outcomes. We fully characterize aneurysmal hemodynamics with converged statistics of platelet RT and shear stress history (SH), to augment the traditional wall shear stress (WSS) on the vascular endothelium. Incorporating microtomographic scans of coil morphology into hemodynamic analysis of coiled intracranial aneurysms, and augmenting traditional analysis with Lagrangian platelet metrics improves CFD predictions, and raises the potential for understanding and clinical translation of computational hemodynamics for intracranial aneurysm treatment outcomes.
随着颅内动脉瘤血管内治疗的频率增加,越来越需要了解线圈栓塞失败的机制。计算流体动力学 (CFD) 模型通常将血管内线圈简化为均质多孔介质 (PM),并专注于血管壁内皮,而不考虑血小板的生物力学环境。这些假设限制了治疗预测计算的准确性。我们使用 X 射线微断层成像对线圈进行了严格的分析,并结合了拉格朗日(血小板)和欧拉(内皮)度量。对四个具有特定患者的、解剖学上准确的体外血流模型进行了动脉瘤治疗,使用相同的特定患者的血管内线圈。对线圈质量形态进行同步加速器断层扫描。在使用特定患者的速度/压力测量值对线圈进行卷曲之前和之后,对动脉瘤的血液动力学进行计算模拟。对于每个患者,我们分析了数千个血小板在几个心动周期内的轨迹,并计算了与血栓形成相关的停留时间 (RT) 和剪切暴露。我们通过比较与线圈分辨 (CR) 模拟的一致性,量化了 PM 方法的不一致性,表明了用于预测治疗结果的关键血液动力学指标的低估或高估。我们通过收敛的血小板 RT 和剪切应力历史 (SH) 统计数据来充分描述动脉瘤的血液动力学特性,以增强血管内皮上的传统壁剪切应力 (WSS)。将线圈形态的微断层扫描纳入颅内动脉瘤线圈治疗的血液动力学分析中,并通过拉格朗日血小板指标对传统分析进行扩充,提高了 CFD 预测的准确性,并提高了理解和临床转化颅内动脉瘤治疗结果计算血液动力学的潜力。