Sunderland K, Wang M, Pandey A S, Gemmete J, Huang Q, Goudge A, Jiang J
Biomedical Engineering, Michigan Technological University, Houghton, MI, USA.
Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX, USA.
Acta Neurochir (Wien). 2021 Aug;163(8):2339-2349. doi: 10.1007/s00701-020-04616-y. Epub 2020 Oct 17.
Surgical intervention for unruptured intracranial aneurysms (IAs) carries inherent health risks. The analysis of "patient-specific" IA geometric and computational fluid dynamics (CFD) simulated wall shear stress (WSS) data has been investigated to differentiate IAs at high and low risk of rupture to help clinical decision making. Yet, outcomes vary among studies, suggesting that novel analysis could improve rupture characterization. The authors describe a CFD analytic method to assess spatiotemporal characteristics of swirling flow vortices within IAs to improve characterization.
CFD simulations were performed for 47 subjects harboring one medium-sized (4-10 mm) middle cerebral artery (MCA) aneurysm with available 3D digital subtraction angiography data. Alongside conventional indices, quantified IA flow vortex spatiotemporal characteristics were applied during statistical characterization. Statistical supervised machine learning using a support vector machine (SVM) method was run with cross-validation (100 iterations) to assess flow vortex-based metrics' strength toward rupture characterization.
Relying solely on vortex indices for statistical characterization underperformed compared with established geometric characteristics (total accuracy of 0.77 vs 0.80) yet showed improvements over wall shear stress models (0.74). However, the application of vortex spatiotemporal characteristics into the combined geometric and wall shear stress parameters augmented model strength for assessing the rupture status of middle cerebral artery aneurysms (0.85).
This preliminary study suggests that the spatiotemporal characteristics of flow vortices within MCA aneurysms are of value to improve the differentiation of ruptured aneurysms from unruptured ones.
未破裂颅内动脉瘤（IA）的手术干预存在内在健康风险。对“患者特异性”IA几何形状和计算流体动力学（CFD）模拟的壁面剪应力（WSS）数据进行分析，以区分破裂风险高和低的IA，从而辅助临床决策。然而，不同研究的结果各异，这表明新的分析方法可能会改善对破裂情况的特征描述。作者描述了一种CFD分析方法，用于评估IA内旋流涡的时空特征，以改进特征描述。
对47例患有一个中型（4 - 10毫米）大脑中动脉（MCA）动脉瘤且有可用3D数字减影血管造影数据的受试者进行CFD模拟。除了传统指标外，在统计特征描述过程中应用了量化的IA流涡时空特征。使用支持向量机（SVM）方法进行统计监督机器学习，并进行交叉验证（100次迭代），以评估基于流涡的指标对破裂特征描述的能力。
与既定的几何特征相比，仅依靠涡旋指标进行统计特征描述的表现较差（总准确率分别为0.77和0.80），但比壁面剪应力模型有所改进（0.74）。然而，将涡旋时空特征应用于几何和壁面剪应力参数的组合中，增强了评估大脑中动脉动脉瘤破裂状态的模型能力（0.85）。
这项初步研究表明，MCA动脉瘤内流涡的时空特征对于改善破裂动脉瘤与未破裂动脉瘤的区分具有价值。
