Department of Fluid Dynamics and Technical Flows, University of Magdeburg, Magdeburg, Germany.
Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.
J Neurointerv Surg. 2018 Mar;10(3):290-296. doi: 10.1136/neurintsurg-2017-012996. Epub 2017 May 2.
Computational fluid dynamics (CFD) blood flow predictions in intracranial aneurysms promise great potential to reveal patient-specific flow structures. Since the workflow from image acquisition to the final result includes various processing steps, quantifications of the individual introduced potential error sources are required.
Three-dimensional (3D) reconstruction of the acquired imaging data as input to 3D model generation was evaluated. Six different reconstruction modes for 3D digital subtraction angiography (DSA) acquisitions were applied to eight patient-specific aneurysms. Segmentations were extracted to compare the 3D luminal surfaces. Time-dependent CFD simulations were carried out in all 48 configurations to assess the velocity and wall shear stress (WSS) variability due to the choice of reconstruction kernel.
All kernels yielded good segmentation agreement in the parent artery; deviations of the luminal surface were present at the aneurysm neck (up to 34.18%) and in distal or perforating arteries. Observations included pseudostenoses as well as noisy surfaces, depending on the selected reconstruction kernel. Consequently, the hemodynamic predictions show a mean SD of 11.09% for the aneurysm neck inflow rate, 5.07% for the centerline-based velocity magnitude, and 17.83%/9.53% for the mean/max aneurysmal WSS, respectively. In particular, vessel sections distal to the aneurysms yielded stronger variations of the CFD values.
The choice of reconstruction kernel for DSA data influences the segmentation result, especially for small arteries. Therefore, if precise morphology measurements or blood flow descriptions are desired, a specific reconstruction setting is required. Furthermore, research groups should be encouraged to denominate the kernel types used in future hemodynamic studies.
颅内动脉瘤的计算流体动力学（CFD）血流预测具有揭示患者特定血流结构的巨大潜力。由于从图像采集到最终结果的工作流程包括各种处理步骤，因此需要对各个引入的潜在误差源进行量化。
评估了作为 3D 模型生成输入的采集成像数据的 3D 重建。将六种不同的 3D 数字减影血管造影（DSA）采集重建模式应用于八个患者特定的动脉瘤。提取分割以比较 3D 管腔表面。对所有 48 种配置进行了时变 CFD 模拟，以评估由于重建核选择引起的速度和壁面剪切应力（WSS）变化。
所有内核在母动脉中均产生良好的分割一致性；在动脉瘤颈部（最高可达 34.18%）和远端或穿透动脉处存在管腔表面的偏差。观察结果包括根据所选重建内核出现的假性狭窄和嘈杂表面。因此，血流动力学预测显示，动脉瘤颈部流入率的平均值标准偏差为 11.09%，基于中心线的速度幅度的平均值标准偏差为 5.07%，平均/最大动脉瘤 WSS 的平均值标准偏差分别为 17.83%/9.53%。特别是，动脉瘤远端的血管段产生了更强的 CFD 值变化。
DSA 数据的重建内核选择会影响分割结果，特别是对于小动脉。因此，如果需要精确的形态测量或血流描述，则需要特定的重建设置。此外，应鼓励研究小组在未来的血流动力学研究中注明所用内核类型。
