Patient-specific computational modeling of cerebral aneurysms with multiple avenues of flow from 3D rotational angiography images.

RATIONALE AND OBJECTIVES Previous studies of aneurysm flow dynamics based on three-dimensional (3D) rotational angiography (RA) images were limited to aneurysms with a single route of blood inflow. However, aneurysms of the circle of Willis frequently involve locations with more than one source of inflow, such as aneurysms of the anterior communicating artery. The highest resolution images of cerebral vessels are from RA images, but this technique is limited to visualizing only one route of inflow at a time, leaving a significant limitation in the application of 3DRA image sets for clinical studies of patient-specific computational fluid dynamics (CFD) simulations. In this report, subject-specific models of cerebral aneurysms with multiple avenues of flow are constructed from RA images by using a novel combination of image co-registration and surface merging techniques. MATERIALS AND METHODS RA images are obtained by means of contrast injection in each vessel that provides inflow to the aneurysm. Anatomic models are constructed independently of each of these vascular trees and fused together into a single model. The model is used to construct a finite element grid for CFD simulations of hemodynamics. RESULTS Three examples of patient-specific models are presented: an anterior communicating artery aneurysm, a basilar tip aneurysm, and a model of an entire circle of Willis with five coincident aneurysms. The method is evaluated with a numeric phantom of an aneurysm in the anterior communicating artery. CONCLUSION These examples show that this new technique can be used to create merged network numeric models for CFD modeling. Furthermore, intra-aneurysmal flow patterns are influenced strongly by merging of the two inflow streams. This effect decreases as distance from the merging streams increases.

[1]  Orlando Soto,et al.  Estimation of the differential pressure at renal artery stenoses , 2004, Magnetic Resonance in Medicine.

[2]  Rainald Löhner,et al.  From medical images to anatomically accurate finite element grids , 2001 .

[3]  Rainald Löhner,et al.  Extensions and improvements of the advancing front grid generation technique , 1996 .

[4]  Fernando Calamante,et al.  Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics , 2003, NeuroImage.

[5]  K. Kayembe,et al.  Cerebral Aneurysms and Variations in the Circle of Willis , 1984, Stroke.

[6]  F. Viñuela,et al.  Intraaneurysmal flow dynamics study featuring an acrylic aneurysm model manufactured using a computerized tomography angiogram as a mold. , 2001, Journal of neurosurgery.

[7]  Alastair J. Martin,et al.  Computational approach to quantifying hemodynamic forces in giant cerebral aneurysms. , 2003, AJNR. American journal of neuroradiology.

[8]  C M Putman,et al.  Computational fluid dynamics modeling of intracranial aneurysms: effects of parent artery segmentation on intra-aneurysmal hemodynamics. , 2006, AJNR. American journal of neuroradiology.

[9]  Peter L. Choyke,et al.  Vessel surface reconstruction with a tubular deformable model , 2001, IEEE Transactions on Medical Imaging.

[10]  Alejandro F. Frangi,et al.  Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity , 2005, IEEE Transactions on Medical Imaging.

[11]  F Asakura,et al.  Evaluation of intraaneurysmal blood velocity by time-density curve analysis and digital subtraction angiography. , 1998, AJNR. American journal of neuroradiology.

[12]  Keisuke Onoda,et al.  Visualization of intraaneurysmal flow patterns with transluminal flow images of 3D MR angiograms in conjunction with aneurysmal configurations. , 2003, AJNR. American journal of neuroradiology.

[13]  James Burgess,et al.  Subject-specific modeling of intracranial aneurysms , 2004, SPIE Medical Imaging.

[14]  Rainald Löhner,et al.  Improving the speed and accuracy of projection-type incompressible flow solvers , 2006 .

[15]  Rainald Löhner,et al.  Blood flow modeling in carotid arteries with computational fluid dynamics and MR imaging. , 2002, Academic radiology.

[16]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[17]  R Löhner,et al.  Merging of intersecting triangulations for finite element modeling. , 2001, Journal of biomechanics.

[18]  C M Strother,et al.  Computer modeling of intracranial saccular and lateral aneurysms for the study of their hemodynamics. , 1995, Neurosurgery.

[19]  P. Yim,et al.  Characterization of shear stress on the wall of the carotid artery using magnetic resonance imaging and computational fluid dynamics. , 2005, Studies in health technology and informatics.

[20]  Shigeka Yoshimoto,et al.  Flow Visualization Studies of Bifurcation Aneurysm , 1999 .

[21]  H. Kikuchi,et al.  Cerebral blood flow patterns at major vessel bifurcations and aneurysms in rats. , 1991, Journal of neurosurgery.

[22]  J. Womersley Method for the calculation of velocity, rate of flow and viscous drag in arteries when the pressure gradient is known , 1955, The Journal of physiology.

[23]  Juan R Cebral,et al.  Computational fluid dynamics modeling of intracranial aneurysms: qualitative comparison with cerebral angiography. , 2007, Academic radiology.

[24]  Rainald Löhner,et al.  Blood-flow models of the circle of Willis from magnetic resonance data , 2003 .

[25]  Peter L. Choyke,et al.  Deformable isosurface and vascular applications , 2002, SPIE Medical Imaging.

[26]  Rainald Löhner,et al.  Automatic unstructured grid generators , 1997 .

[27]  R. Löhner Regridding Surface Triangulations , 1996 .

[28]  Thomas J. R. Hughes,et al.  Finite element modeling of blood flow in arteries , 1998 .

[29]  Y. Cho,et al.  Intracranial aneurysms: flow analysis of their origin and progression. , 1992, AJNR. American journal of neuroradiology.

[30]  C. Putman,et al.  Patient-specific computational fluid dynamics modeling of anterior communicating artery aneurysms: a study of the sensitivity of intra-aneurysmal flow patterns to flow conditions in the carotid arteries. , 2006, AJNR. American journal of neuroradiology.

[31]  C. Putman,et al.  Characterization of cerebral aneurysms for assessing risk of rupture by using patient-specific computational hemodynamics models. , 2005, AJNR. American journal of neuroradiology.

[32]  J. L. Counord,et al.  In vitro study of haemodynamics in a giant saccular aneurysm model: influence of flow dynamics in the parent vessel and effects of coil embolisation , 1994, Neuroradiology.