The Computational Fluid Dynamics Rupture Challenge 2013--Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms.

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent "outliers" (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Patient-specific modelling of abdominal aortic aneurysms: The influence of wall thickness on predicted clinical outcomes.

Rupture of abdominal aortic aneurysms (AAAs) is linked to aneurysm morphology. This study investigates the influence of patient-specific (PS) AAA wall thickness on predicted clinical outcomes. Eight patients under surveillance for AAAs were selected from the MA(3)RS clinical trial based on the complete absence of intraluminal thrombus. Two finite element (FE) models per patient were constructed; the first incorporated variable wall thickness from CT (PS_wall), and the second employed a 1.9mm uniform wall (Uni_wall). Mean PS wall thickness across all patients was 1.77±0.42mm. Peak wall stress (PWS) for PS_wall and Uni_wall models was 0.6761±0.3406N/mm(2) and 0.4905±0.0850N/mm(2), respectively. In 4 out of 8 patients the Uni_wall underestimated stress by as much as 55%; in the remaining cases it overestimated stress by up to 40%. Rupture risk more than doubled in 3 out of 8 patients when PS_wall was considered. Wall thickness influenced the location and magnitude of PWS as well as its correlation with curvature. Furthermore, the volume of the AAA under elevated stress increased significantly in AAAs with higher rupture risk indices. This highlights the sensitivity of standard rupture risk markers to the specific wall thickness strategy employed.

Change in aneurysmal flow pulsatility after flow diverter treatment.

MOTIVATION: Treatment of intracranial aneurysms with flow diverters (FDs) has recently become an attractive alternative. Although considerable effort has been devoted to understand their effects on the time-averaged or peak systolic flow field, no previous study has analyzed the variability of FD-induced flow reduction along the cardiac cycle. METHODS: Fourteen saccular aneurysms, candidates for FD treatment because of their morphology, located on the internal carotid artery were virtually treated with FDs and pre- and post-treatment blood flow was simulated with CFD techniques. Common hemodynamic variables were recorded at each time step of the cardiac cycle and differences between the untreated and treated models were assessed. RESULTS: Flow pulsatility, expressed by the pulsatility index (PI) of the velocity, significantly increased (36.0%; range: 14.6-88.3%) after FD treatment. Peak systole velocity reduction was significantly smaller (30.5%; range: 19.6-51.0%) than time-averaged velocity reduction (43.0%; range: 29.1-69.8%). No changes were observed in the aneurysmal pressure. CONCLUSIONS: FD-induced flow reduction varies considerably during the cardiac cycle. FD treatment significantly increased the flow pulsatility in the aneurysm.

Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability.

Adverse wall shear stress (WSS) patterns are known to play a key role in the localisation, formation, and progression of intracranial aneurysms (IAs). Complex region-specific and time-varying aneurysmal WSS patterns depend both on vascular morphology as well as on variable systemic flow conditions. Computational fluid dynamics (CFD) has been proposed for characterising WSS patterns in IAs; however, CFD simulations often rely on deterministic boundary conditions that are not representative of the actual variations in blood flow. We develop a data-driven statistical model of internal carotid artery (ICA) flow, which is used to generate a virtual population of waveforms used as inlet boundary conditions in CFD simulations. This allows the statistics of the resulting aneurysmal WSS distributions to be computed. It is observed that ICA waveform variations have limited influence on the time-averaged WSS (TAWSS) on the IA surface. In contrast, in regions where the flow is locally highly multidirectional, WSS directionality and harmonic content are strongly affected by the ICA flow waveform. As a consequence, we argue that the effect of blood flow variability should be explicitly considered in CFD-based IA rupture assessment to prevent confounding the conclusions.

Effect of aneurysm and ICA morphology on hemodynamics before and after flow diverter treatment.

BACKGROUND: Flow diverter (FD) treatment aims to slow down blood flow inside the aneurysm and increase the average time that blood resides in the aneurysm. OBJECTIVE: To investigate the relationship between vessel and aneurysm morphology and their influence on the way in which braided FDs change intra-aneurysmal hemodynamics. MATERIALS AND METHODS: Twenty-three patient-specific intracranial aneurysm models at the supraclinoid segment of the internal carotid artery were studied. Vessel and aneurysm morphology was quantified and blood flow was modeled with computational fluid dynamics simulations. The relation between morphologic variables and the hemodynamic variables, WSS (wall shear stress) and totime (ratio between the aneurysm volume and inflow at the aneurysm neck), was assessed statistically. RESULTS: Intra-aneurysmal flow was less dependent on the vessel than on aneurysm morphology. In summary, after treatment with a FD, a greater aneurysm flow reduction and redirection to the vessel main stream should be expected for (a) aneurysms located further away from the curvature peak, (b) aneurysms on the inner side of the bend, (c) aneurysms with no proximal stenosis, and (d) larger aneurysms. CONCLUSIONS: Although the change in intra-aneurysmal hemodynamics after FD treatment strongly depends on the morphology of the aneurysm, the hemodynamic effect of a FD is also linked to the parent vessel morphology and the position and orientation of the aneurysm with respect to it.

Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets.

Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

Newtonian and non-Newtonian blood flow in coiled cerebral aneurysms.

Endovascular coiling aims to isolate the aneurysm from blood circulation by altering hemodynamics inside the aneurysm and triggering blood coagulation. Computational fluid dynamics (CFD) techniques have the potential to predict the post-operative hemodynamics and to investigate the complex interaction between blood flow and coils. The purpose of this work is to study the influence of blood viscosity on hemodynamics in coiled aneurysms. Three image-based aneurysm models were used. Each case was virtually coiled with a packing density of around 30%. CFD simulations were performed in coiled and untreated aneurysm geometries using a Newtonian and a Non-Newtonian fluid models. Newtonian fluid slightly overestimates the intra-aneurysmal velocity inside the aneurysm before and after coiling. There were numerical differences between fluid models on velocity magnitudes in coiled simulations. Moreover, the non-Newtonian fluid model produces high viscosity (>0.007 [Pas]) at aneurysm fundus after coiling. Nonetheless, these local differences and high-viscous regions were not sufficient to alter the main flow patterns and velocity magnitudes before and after coiling. To evaluate the influence of coiling on intra-aneurysmal hemodynamics, the assumption of a Newtonian fluid can be used.

Analysis and quantification of endovascular coil distribution inside saccular aneurysms using histological images.

OBJECTIVE: Endovascular coiling is often performed by first placing coils along the aneurysm wall to create a frame and then by filling up the aneurysm core. However, little attention has been paid to quantifying this filling strategy and to see how it changes for different packing densities. The purpose of this work is to analyze and quantify endovascular coil distribution inside aneurysms based on serial histological images of experimental aneurysms. METHOD: Seventeen histological images from 10 elastase-induced saccular aneurysms in rabbits treated with coils were studied. In-slice coil density, defined as the area taken up by coil winds, was calculated on each histological image. Images were analyzed by partitioning the aneurysm along its longitudinal and radial axes. Coil distribution was quantified by measuring and comparing the in-slice coil density of each partition. RESULTS: Mean total in-slice coil density was 22.0 ± 6.2% (range 10.1-30.2%). The density was non-significantly different (p = 0.465) along the longitudinal axis. A significant difference (p < 0.001) between peripheral and core densities was found. Additionally, the peripheral-core density ratio was observed to be inversely proportional to the total in-slice coil density (R(2)=0.57, p <0.001). This ratio was near unity for high in-slice coil density (around 30%). CONCLUSIONS: These findings demonstrate and confirm that coils tend to be located near the aneurysm periphery when few are inserted. However, when more coils are added, the radial distribution becomes more homogeneous. Coils are homogeneously distributed along the longitudinal axis.

A virtual coiling technique for image-based aneurysm models by dynamic path planning.

Computational algorithms modeling the insertion of endovascular devices, such as coil or stents, have gained an increasing interest in recent years. This scientific enthusiasm is due to the potential impact that these techniques have to support clinicians by understanding the intravascular hemodynamics and predicting treatment outcomes. In this work, a virtual coiling technique for treating image-based aneurysm models is proposed. A dynamic path planning was used to mimic the structure and distribution of coils inside aneurysm cavities, and to reach high packing densities, which is desirable by clinicians when treating with coils. Several tests were done to evaluate the performance on idealized and image-based aneurysm models. The proposed technique was validated using clinical information of real coiled aneurysms. The virtual coiling technique reproduces the macroscopic behavior of inserted coils and properly captures the densities, shapes and coil distributions inside aneurysm cavities. A practical application was performed by assessing the local hemodynamic after coiling using computational fluid dynamics (CFD). Wall shear stress and intra-aneurysmal velocities were reduced after coiling. Additionally, CFD simulations show that coils decrease the amount of contrast entering the aneurysm and increase its residence time.

AngioLab--a software tool for morphological analysis and endovascular treatment planning of intracranial aneurysms.

Determining whether and how an intracranial aneurysm should be treated is a tough decision that clinicians face everyday. Emerging computational tools could help clinicians analyze clinical data and make these decisions. AngioLab is a single graphical user interface, developed on top of the open source framework GIMIAS, that integrates some of the latest image analysis and computational modeling tools for intracranial aneurysms. Two workflows are available: Advanced Morphological Analysis (AMA) and Endovascular Treatment Planning (ETP). AngioLab has been evaluated by a total of 62 clinicians, who considered the information provided by AngioLab relevant and meaningful. They acknowledged the emerging need of these type of tools and the potential impact they might have on the clinical decision-making process.