Rationale and Objective
The purpose of this study is to determine whether computational fluid dynamics modeling can correctly predict the location of the major intra-aneurysmal flow structures that can be identified by conventional angiography.
Materials and Methods
Patient-specific models of three cerebral aneurysms were constructed from three-dimensional rotational angiography images and computational fluid dynamic simulations performed. Using these velocity fields, contrast transport was simulated and visualizations constructed to provide a “virtual” angiogram. These models were then compared to images from high frame rate conventional angiography to compare flow structures.
Results
Computational fluid dynamics simulations showed three distinct flow types ranging from simple to complex. Virtual angiographic images showed good agreement with images from conventional angiography for all three aneurysms with analogous size and orientation of the inflow jet, regions of impaction, and flow type. Large intra-aneurysmal vortices and regions of outflow also corresponded between the images.
Conclusions
Patient-specific image-based computational models of cerebral aneurysms can realistically reproduce the major intra-aneurysmal flow structures observed with conventional angiography. The agreement between computational models and angiographic structures is less for slower zones of recirculation later in the cardiac cycle.
Cerebral aneurysms are widely believed to form and grow on the basis of hemodynamic interactions with the wall biology. Researchers have used a variety of tools to study these complex biological phenomena including animal, in vitro models, and computational models ( ). The goal of these experiments has been to approximate the in vivo environment so that theories can be developed and further tested in more realistic systems. Ultimately, a link between the research and the clinical outcomes (i.e., aneurysmal growth and rupture) is necessary to reach an understanding of the relative importance of the forces or mechanisms that are discovered. Previously used methods fall short of this goal because they cannot reproduce the in vivo state of a particular patient. Until recently, computational studies have been only performed on idealized aneurysm geometries or approximations of a specific patient geometry. As computational techniques have improved, more refined models have been constructed, leading to a transition from idealized geometries to “realistic” models based on typical patient anatomies. Most recently, studies have tried to replicate the exact anatomy of specific patients to connect specific hemodynamic factors to clinical events, allowing statistical analysis in a patient population ( ). Despite these more sophisticated methods, one question still remains: Are these models accurately reproducing what is occurring in a specific patient?
Without accurate in vivo measurements for comparison, a complete validation of computational methods is not possible. But, there is growing evidence that computational methods are reliably reproducing the in vivo intravascular environment. Comparisons with a variety of simplified experimental biological systems have shown good correlation with computational models ( ). In addition to this direct validation, sensitivity analysis has been performed to obtain an understanding of the influence of a variable within a physiologic range are on the results of a specific model ( ). These analyses have been done to test a variety of assumptions used in modeling including flow rates, flow asymmetries, inflow boundary conditions, Newtonian properties of fluids, reconstruction/grid generation techniques, and small branch segmentation ( ). Even without knowing the exact value of the input variable, this analysis can measure the influence of changes (or errors) in the variable would have on the results of a simulation. Previous work has shown that inaccuracies in geometry have the largest potential for adversely influencing intra-aneurysmal hemodynamics ( ). Other variables appear to have only minor effects on the models and are considered higher order variables.
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Methods
Patients and Images
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Table 1
Cases selected for study
Patient Location Size (mm) Rupture Flow type 1 LICA 25 Yes IV 2 LICA 11 No III 3 RMCA 5 Yes I
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Vascular Models
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Blood Flow Models
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Contrast Transport Models
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Postprocessing and Visualization
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Results
Patient 1
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Patient 2
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Patient 3
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Discussion
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Conclusions
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