Does the DSA reconstruction kernel affect hemodynamic predictions in intracranial aneurysms? An analysis of geometry and blood flow variations.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Fluid-Structure Simulations of a Ruptured Intracranial Aneurysm: Constant versus Patient-Specific Wall Thickness.

Computational Fluid Dynamics is intensively used to deepen the understanding of aneurysm growth and rupture in order to support physicians during therapy planning. However, numerous studies considering only the hemodynamics within the vessel lumen found no satisfactory criteria for rupture risk assessment. To improve available simulation models, the rigid vessel wall assumption has been discarded in this work and patient-specific wall thickness is considered within the simulation. For this purpose, a ruptured intracranial aneurysm was prepared ex vivo, followed by the acquisition of local wall thickness using µCT. The segmented inner and outer vessel surfaces served as solid domain for the fluid-structure interaction (FSI) simulation. To compare wall stress distributions within the aneurysm wall and at the rupture site, FSI computations are repeated in a virtual model using a constant wall thickness approach. Although the wall stresses obtained by the two approaches-when averaged over the complete aneurysm sac-are in very good agreement, strong differences occur in their distribution. Accounting for the real wall thickness distribution, the rupture site exhibits much higher stress values compared to the configuration with constant wall thickness. The study reveals the importance of geometry reconstruction and accurate description of wall thickness in FSI simulations.

The Computational Fluid Dynamics Rupture Challenge 2013­Phase I: prediction of rupture status in intracranial aneurysms.

BACKGROUND AND PURPOSE: Rupture risk assessment for intracranial aneurysms remains challenging, and risk factors, including wall shear stress, are discussed controversially. The primary purpose of the presented challenge was to determine how consistently aneurysm rupture status and rupture site could be identified on the basis of computational fluid dynamics. MATERIALS AND METHODS: Two geometrically similar MCA aneurysms were selected, 1 ruptured, 1 unruptured. Participating computational fluid dynamics groups were blinded as to which case was ruptured. Participants were provided with digitally segmented lumen geometries and, for this phase of the challenge, were free to choose their own flow rates, blood rheologies, and so forth. Participants were asked to report which case had ruptured and the likely site of rupture. In parallel, lumen geometries were provided to a group of neurosurgeons for their predictions of rupture status and site. RESULTS: Of 26 participating computational fluid dynamics groups, 21 (81%) correctly identified the ruptured case. Although the known rupture site was associated with low and oscillatory wall shear stress, most groups identified other sites, some of which also experienced low and oscillatory shear. Of the 43 participating neurosurgeons, 39 (91%) identified the ruptured case. None correctly identified the rupture site. CONCLUSIONS: Geometric or hemodynamic considerations favor identification of rupture status; however, retrospective identification of the rupture site remains a challenge for both engineers and clinicians. A more precise understanding of the hemodynamic factors involved in aneurysm wall pathology is likely required for computational fluid dynamics to add value to current clinical decision-making regarding rupture risk.

Accuracy and reproducibility of patient-specific hemodynamic models of stented intracranial aneurysms: report on the Virtual Intracranial Stenting Challenge 2011.

Validation studies are prerequisites for computational fluid dynamics (CFD) simulations to be accepted as part of clinical decision-making. This paper reports on the 2011 edition of the Virtual Intracranial Stenting Challenge. The challenge aimed to assess the reproducibility with which research groups can simulate the velocity field in an intracranial aneurysm, both untreated and treated with five different configurations of high-porosity stents. Particle imaging velocimetry (PIV) measurements were obtained to validate the untreated velocity field. Six participants, totaling three CFD solvers, were provided with surface meshes of the vascular geometry and the deployed stent geometries, and flow rate boundary conditions for all inlets and outlets. As output, they were invited to submit an abstract to the 8th International Interdisciplinary Cerebrovascular Symposium 2011 (ICS'11), outlining their methods and giving their interpretation of the performance of each stent configuration. After the challenge, all CFD solutions were collected and analyzed. To quantitatively analyze the data, we calculated the root-mean-square error (RMSE) over uniformly distributed nodes on a plane slicing the main flow jet along its axis and normalized it with the maximum velocity on the slice of the untreated case (NRMSE). Good agreement was found between CFD and PIV with a NRMSE of 7.28%. Excellent agreement was found between CFD solutions, both untreated and treated. The maximum difference between any two groups (along a line perpendicular to the main flow jet) was 4.0 mm/s, i.e. 4.1% of the maximum velocity of the untreated case, and the average NRMSE was 0.47% (range 0.28-1.03%). In conclusion, given geometry and flow rates, research groups can accurately simulate the velocity field inside an intracranial aneurysm-as assessed by comparison with in vitro measurements-and find excellent agreement on the hemodynamic effect of different stent configurations.

Automatic Detection and Visualization of Qualitative Hemodynamic Characteristics in Cerebral Aneurysms.

Cerebral aneurysms are a pathological vessel dilatation that bear a high risk of rupture. For the understanding and evaluation of the risk of rupture, the analysis of hemodynamic information plays an important role. Besides quantitative hemodynamic information, also qualitative flow characteristics, e.g., the inflow jet and impingement zone are correlated with the risk of rupture. However, the assessment of these two characteristics is currently based on an interactive visual investigation of the flow field, obtained by computational fluid dynamics (CFD) or blood flow measurements. We present an automatic and robust detection as well as an expressive visualization of these characteristics. The detection can be used to support a comparison, e.g., of simulation results reflecting different treatment options. Our approach utilizes local streamline properties to formalize the inflow jet and impingement zone. We extract a characteristic seeding curve on the ostium, on which an inflow jet boundary contour is constructed. Based on this boundary contour we identify the impingement zone. Furthermore, we present several visualization techniques to depict both characteristics expressively. Thereby, we consider accuracy and robustness of the extracted characteristics, minimal visual clutter and occlusions. An evaluation with six domain experts confirms that our approach detects both hemodynamic characteristics reasonably.