Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity

Hemodynamic factors are thought to be implicated in the progression and rupture of intracranial aneurysms. Current efforts aim to study the possible associations of hemodynamic characteristics such as complexity and stability of intra-aneurysmal flow patterns, size and location of the region of flow impingement with the clinical history of aneurysmal rupture. However, there are no reliable methods for measuring blood flow patterns in vivo. In this paper, an efficient methodology for patient-specific modeling and characterization of the hemodynamics in cerebral aneurysms from medical images is described. A sensitivity analysis of the hemodynamic characteristics with respect to variations of several variables over the expected physiologic range of conditions is also presented. This sensitivity analysis shows that although changes in the velocity fields can be observed, the characterization of the intra-aneurysmal flow patterns is not altered when the mean input flow, the flow division, the viscosity model, or mesh resolution are changed. It was also found that the variable that has the greater impact on the computed flow fields is the geometry of the vascular structures. We conclude that with the proposed modeling pipeline clinical studies involving large numbers cerebral aneurysms are feasible.

[1]  Peter J. Yim,et al.  Multimodality image-based models of carotid artery hemodynamics (Cum Laude Poster Award) , 2004, SPIE Medical Imaging.

[2]  A. Algra,et al.  Incidence of subarachnoid hemorrhage: role of region, year, and rate of computed tomography: a meta-analysis. , 1996, Stroke.

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

[4]  S. Shibata,et al.  Incidence and Outcome of Multiple Intracranial Aneurysms in a Defined Population , 2003, Stroke.

[5]  Johnson Huang,et al.  The Probability of Sudden Death from Rupture of Intracranial Aneurysms: A Meta-analysis , 2002, Neurosurgery.

[6]  Gavin W Britz,et al.  Prevalence of asymptomatic incidental aneurysms: review of 4568 arteriograms. , 2002, Journal of neurosurgery.

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

[8]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[9]  Rainald Loehner,et al.  Renumbering strategies for unstructured-grid solvers operating on shared-memory, cache-based parallel machines , 1997 .

[10]  M M Hafez Numerical Simulations of Incompressible Flows , 2003 .

[11]  Alejandro F. Frangi,et al.  COMPUTATIONAL ANALYSIS OF BLOOD FLOW DYNAMICS IN CEREBRAL ANEURYSMS FROM CTA AND 3 D ROTATIONAL ANGIOGRAPHY IMAGE DATA , 2003 .

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

[13]  T. Liou,et al.  A review on in vitro studies of hemodynamic characteristics in terminal and lateral aneurysm models. , 1999, Proceedings of the National Science Council, Republic of China. Part B, Life sciences.

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

[15]  Rainald Loehner,et al.  Interactive On-Line Visualization and Collaboration for Parallel Unstructured Multidisciplinary Applications , 1998 .

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

[17]  T. Yoshimoto,et al.  Hemodynamic analysis of an adult vein of Galen aneurysm malformation by use of 3D image-based computational fluid dynamics. , 2003, AJNR. American journal of neuroradiology.

[18]  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.

[19]  Stehbens We,et al.  Intracranial arterial aneurysms. , 1954 .

[20]  Rainald Loehner,et al.  Finite element modeling of the Circle of Willis from magnetic resonance data , 2003, SPIE Medical Imaging.

[21]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

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

[23]  Peter L. Choyke,et al.  Isosurfaces as deformable models for magnetic resonance angiography , 2003, IEEE Transactions on Medical Imaging.

[24]  J. Rae,et al.  Implementation of finite element Methods for Navier-Stokes equations , 1982 .

[25]  Monica Hernandez,et al.  Pre-clinical evaluation of implicit deformable models for three-dimensional segmentation of brain aneurysms from CTA images , 2003, SPIE Medical Imaging.

[26]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

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

[28]  Peter J. Yim,et al.  Estimation of pressure gradients at renal artery stenoses , 2003, SPIE Medical Imaging.

[29]  Rainald Löhner,et al.  A stabilized edge-based implicit incompressible flow formulation , 2004 .

[30]  D. Holdsworth,et al.  Image-based computational simulation of flow dynamics in a giant intracranial aneurysm. , 2003, AJNR. American journal of neuroradiology.

[31]  F. Tomasello,et al.  Asymptomatic aneurysms. Literature meta-analysis and indications for treatment. , 1998, Journal of neurosurgical sciences.

[32]  Christopher M. Putman,et al.  Cerebral aneurysm hemodynamics modeling from 3D rotational angiography , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[33]  A D Hughes,et al.  Blood flow and vessel mechanics in a physiologically realistic model of a human carotid arterial bifurcation. , 2000, Journal of biomechanics.

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

[35]  A. Algra,et al.  Case-fatality rates and functional outcome after subarachnoid hemorrhage: a systematic review. , 1997, Stroke.

[36]  R. Tatarelli,et al.  Peduncular hallucinosis following a transoral odontoidectomy for cranio-vertebral junction malformation. A case report. , 1998, Journal of neurosurgical sciences.

[37]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

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

[39]  Rainald Löhner Renumbering strategies for unstructured-grid solvers operating on shared-memory, cache-based parallel machines , 1998 .

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

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

[42]  R. Metcalfe The promise of computational fluid dynamics as a tool for delineating therapeutic options in the treatment of aneurysms. , 2003, AJNR. American journal of neuroradiology.

[43]  M. Limburg,et al.  Mortality and morbidity of surgery for unruptured intracranial aneurysms: a meta-analysis. , 1998, Stroke.

[44]  T. Yoshimoto,et al.  Computational simulation of therapeutic parent artery occlusion to treat giant vertebrobasilar aneurysm. , 2004, AJNR. American journal of neuroradiology.

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