Review
Current status of computational fluid dynamics for cerebral aneurysms: The clinician’s perspective

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Abstract

The ultimate management goal for unruptured intracranial aneurysms is to select the aneurysms at risk of rupture and treat them. Computational fluid dynamics (CFD) utilizes mechanical engineering principles to explicate what occurs in tubes (vessels) and bulges (aneurysms). CFD parameters have been related to the biological processes that occur in the aneurysm wall, and models have been developed to predict the risk of aneurysm rupture. A PubMed search from 1 January 1970 to 30 November 2010 was carried out using the keywords “computational fluid dynamics” AND “cerebral aneurysm”. References were also reviewed for relevant articles. All relevant articles were then reviewed by a vascular neurosurgeon, who found that the hemodynamic parameters of wall shear stress (WSS), WSS gradient, inflow jet, impingement zone, and aneurysm inflow-angle (IA) lack the predictive values required for clinical practice. CFD study can now be simulated and reproduced in a simple and fast analysis of steady, non-pulsatile flow with phase contrast magnetic resonance-derived volumetric inflow rate but the key question of whether a patient-specific CFD model can predict the rupture risk of unruptured intracranial aneurysms remains to be determined in future studies incorporating multivariate analysis. CFD models will become available for routine clinical practice as the computational power of computers further improves.

Section snippets

Background

Aneurysmal subarachnoid hemorrhage remains an important cause of stroke mortality and morbidity.[1], [2] Securing aneurysms to prevent hemorrhage is one of the major goals in patient management. However, the prevalence of unruptured intracranial aneurysms has been reported to be as high as 6.5%, and only a minority of these aneurysms eventually rupture.3 Unselected treatment can be harmful.4 Hence, the precise identification of aneurysm risk profiles is of paramount importance in counseling

Computational fluid dynamics models

In patient-specific models, vascular and aneurysm geometry acquisition is the first step. High-resolution, three-dimensional angiographic data can be acquired through catheter angiography,[7], [8], [9], [10], [11], [12] CT angiography,[13], [14] or magnetic resonance angiography (MRA).[15], [16], [17], [18], [19] In catheter angiography, rotational imaging synchronized with contrast injection at a high frame rate is typically acquired and then reconstructed into three-dimensional voxel data. In

Intra-aneurysm flow types

The most commonly used intra-aneurysmal flow type classification is that proposed by Cebral et al. in 2005.7 Their classification includes four flow types according to the complexity and stability of the flow during the cardiac cycle: type I describes an unchanging direction of the inflow jet with a single associated vortex; type II describes an unchanging direction of the inflow jet with multiple associated vortices during the cardiac cycle; type III describes a changing direction of the

Wall shear stress, impingement zone, and inflow jet

Wall shear stress (WSS) (Fig. 1) is the tangential force applied to the vessel wall by the blood flow, or can be viewed as the frictional force applied to the vessel wall.22 The assumption that a high degree of WSS causes aneurysm formation is intuitive, but the situation is more complex. Variation in the circle of Willis geometry and local branch asymmetries leads to an increase in WSS levels at the branching points, which coincides with the locations at which most intracranial aneurysms are

Aneurysm inflow-angle

Considerable interest has also been accorded the morphological relationship between aneurysm and the parent vessel because of the association between it and the intra-aneurysmal flow pattern. In the comparative study carried out by Baharoglu et al., multivariate logistic regression identified the aneurysm inflow-angle (IA) (Fig. 2) as an independent morphological discriminant of rupture status.32 CFD analysis in an idealized model showed that increasing the IA leads to deeper migration of the

Coiling

Little data exist for CFD analysis after coiling. Schirmer and Malek investigated simulated embolization with one or more computer-designed helical coils in a spherical sidewall aneurysm on a curved parent vessel.33 Their CFD analysis showed intra-aneurysmal flow and energy flux into the dome to be significantly reduced by coiling, which also decreased the WSS and WSS gradient. Interestingly, these effects were dependent on the coil orientation, with the effectiveness order being parallel > 

Future directions

First-generation intracranial stents with 2–4 mm diameter pores have already been shown to reduce peak velocities and the strength of intra-aneurysmal flow vortices, especially at the end of the cardiac cycle.[35], [36], [37] Flow diverters, which have a lower degree of porosity and greater pore density, can further reduce the inflow into an aneurysm.38 Clinically, we have already begun our journey toward incorporating flow diverters into the treatment of intracranial aneurysms.39 Important

Conclusions

CFD study can be simulated in a simple and fast analysis of steady, non-pulsatile flow with phase contrast magnetic resonance-derived volumetric inflow rate.[41], [42] Reproducibility of simulations across different techniques and research teams in both patient-specific non-stented and stented models was recently confirmed.[43], [44] CFD models will become increasingly available for routine clinical practice as the computational power of computers further improves. Aneurysm location and

Acknowledgment

The authors would like to acknowledge the editorial service provided by Armstrong-Hilton.

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      There are several methods to assess the complexity of hemodynamic forces in vivo, and most use models based on the real anatomy of human aneurysms.8,21,22 Among these, computed fluid dynamics (CFD) is able to investigate fluidodynamics by a computed simulation of vascular stress flow inside an IA.23-27 CFD is a technique that is able to make virtual vectors, also known as streamlines, by data analysis from CT and MR angiography or rotational DSA.11,16,20,28-32

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