Valen-Sendstad Kristian, Bergersen Aslak W, Shimogonya Yuji, Goubergrits Leonid, Bruening Jan, Pallares Jordi, Cito Salvatore, Piskin Senol, Pekkan Kerem, Geers Arjan J, Larrabide Ignacio, Rapaka Saikiran, Mihalef Viorel, Fu Wenyu, Qiao Aike, Jain Kartik, Roller Sabine, Mardal Kent-Andre, Kamakoti Ramji, Spirka Thomas, Ashton Neil, Revell Alistair, Aristokleous Nicolas, Houston J Graeme, Tsuji Masanori, Ishida Fujimaro, Menon Prahlad G, Browne Leonard D, Broderick Stephen, Shojima Masaaki, Koizumi Satoshi, Barbour Michael, Aliseda Alberto, Morales Hernán G, Lefèvre Thierry, Hodis Simona, Al-Smadi Yahia M, Tran Justin S, Marsden Alison L, Vaippummadhom Sreeja, Einstein G Albert, Brown Alistair G, Debus Kristian, Niizuma Kuniyasu, Rashad Sherif, Sugiyama Shin-Ichiro, Owais Khan M, Updegrove Adam R, Shadden Shawn C, Cornelissen Bart M W, Majoie Charles B L M, Berg Philipp, Saalfield Sylvia, Kono Kenichi, Steinman David A
Simula Research Laboratory and Center for Cardiological Innovation, Lysaker, Norway.
University of Oslo, Oslo, Norway.
Cardiovasc Eng Technol. 2018 Dec;9(4):544-564. doi: 10.1007/s13239-018-00374-2. Epub 2018 Sep 10.
Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline.
3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters.
A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability.
Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.
基于图像的计算流体动力学（CFD）被广泛用于预测颅内动脉瘤壁面切应力（WSS），尤其是旨在改善破裂风险评估。然而，人们对预测的WSS的变异性以及与破裂的不一致关联表示担忧。先前的挑战以及个别研究团队的研究都集中在基于图像的CFD流程的各个方面。本次挑战赛的目的是量化整个流程的总体变异性。
向参与者提供了五个大脑中动脉动脉瘤的三维旋转血管造影图像数据集，参与者可以自由选择其分割方法、边界条件以及CFD求解器和设置。要求参与者填写一份关于其求解策略和动脉瘤CFD经验的问卷，并提供WSS大小的表面分布，我们据此客观地得出了各种血流动力学参数。
共提交了28个数据集，来自26个团队，这些团队的自我评估经验水平各不相同。分割、CFD模型范围和流入率的广泛变异性导致瘤腔平均WSS的四分位间距高达56%，在通过母动脉WSS进行归一化后降至<30%。瘤腔最大WSS和低切应力区域的变异性更大，而按低切应力或高切应力对病例进行排序在各团队之间仅显示出适度的一致性。经验并不是变异性的显著预测因素。
颅内动脉瘤WSS的预测存在广泛变异性。虽然分割和CFD求解器技术可能难以在不同团队之间实现标准化，但我们的研究结果表明，通过建立模型范围、流入率和血液属性的指导原则，并鼓励报告归一化的血流动力学参数，可以减少基于图像的CFD中的一些变异性。
