Comprehensive validation of computational fluid dynamics simulations of in-vivo blood flow in patient-specific cerebral aneurysms.

PURPOSE Recently, image-based computational fluid dynamic (CFD) simulations have been proposed to investigate the local hemodynamics inside human cerebral aneurysms. It was suggested that the knowledge of the computed three-dimensional flow fields can be used to assist clinical risk assessment and treatment decision making. Therefore, it was desired to know the reliability of CFD for cerebral blood flow simulation, and be able to provide clinical feedback. However, the validations were not yet comprehensive as they lack either patient-specific boundary conditions (BCs) required for CFD simulations or quantitative comparison methods. METHODS In this study, based on a recently proposed in-vitro quantitative CFD evaluation approach via virtual angiography, the CFD evaluation was extended from phantom to patient studies. In contrast to previous work, patient-specific blood flow rates obtained by transcranial color coded Doppler ultrasound measurements were used to impose CFD BCs. Virtual angiograms (VAs) were constructed which resemble clinically acquired angiograms (AAs). Quantitative measures were defined to thoroughly evaluate the correspondence of the detailed flow features between the AAs and the VAs, and thus, the reliability of CFD simulations. RESULTS The proposed simulation pipeline provided a comprehensive validation method of CFD simulation for reproducing cerebral blood flow, with a focus on the aneurysm region. Six patient cases were tested and close similarities were found in terms of spatial and temporal variations of contrast agent (CA) distribution between AAs and VAs. For patient #1 to #5, discrepancies of less than 11% were found for the relative root mean square errors in time intensity curve comparisons from characteristic vasculature positions. For patient #6, where the CA concentration curve at vessel inlet cannot be directly extracted from the AAs and given as a BC, deviations about 20% were found. CONCLUSIONS As a conclusion, the reliability of the CFD simulations was well confirmed. Besides, it was shown that the accuracy of CFD simulations was closely related to the input BCs.

[1]  R. Takolander,et al.  Changes in middle cerebral artery flow velocity and pulsatility index after carotid endarterectomy. , 1991, European journal of vascular surgery.

[2]  J. Mocco,et al.  Hemodynamic–Morphologic Discriminants for Intracranial Aneurysm Rupture , 2011, Stroke.

[3]  G. Duckwiler,et al.  Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling. , 2009, Medical physics.

[4]  K. Katada,et al.  Magnitude and Role of Wall Shear Stress on Cerebral Aneurysm: Computational Fluid Dynamic Study of 20 Middle Cerebral Artery Aneurysms , 2004, Stroke.

[5]  Andrea Giachetti,et al.  3D Reconstruction of Large Tubular Geometries from CT Data , 2003, IS4TH.

[6]  R. Close,et al.  X-ray videodensitometric methods for blood flow and velocity measurement: a critical review of literature. , 2000, Medical physics.

[7]  大輔 川口,et al.  D108 脳動脈瘤内部の流動パターンのIn Vitro PIV計測とCFD解析 , 2004 .

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

[9]  Alastair J. Martin,et al.  Aneurysm Growth Occurs at Region of Low Wall Shear Stress: Patient-Specific Correlation of Hemodynamics and Growth in a Longitudinal Study , 2008, Stroke.

[10]  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.

[11]  E. D. Jacobson,et al.  Hemodynamic effects of rapid injection into the canine superior mesenteric artery , 1981, Digestive Diseases and Sciences.

[12]  Til Aach,et al.  Toward quantitative virtual angiography: Evaluation with in vitro studies , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  Matthias Bertram,et al.  Experimental validation and sensitivity analysis for CFD simulations of cerebral aneurysms , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[14]  Gene H. Golub,et al.  Optimal Surface Smoothing as Filter Design , 1996, ECCV.

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

[16]  Hui Meng,et al.  Validation of CFD simulations of cerebral aneurysms with implication of geometric variations. , 2006, Journal of biomechanical engineering.

[17]  Yiannis Ventikos,et al.  Pulsatile Blood Flow in Anatomically Accurate Vessels with Multiple Aneurysms: A Medical Intervention Planning Application of Computational Haemodynamics , 2003 .

[18]  David A. Steinman,et al.  Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics , 2005, IEEE Transactions on Medical Imaging.

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

[20]  I. Wächter,et al.  3D reconstruction of cerebral blood flow and vessel morphology from x-ray rotational angiography , 2009 .

[21]  A. Valencia,et al.  Blood flow dynamics in patient-specific cerebral aneurysm models: the relationship between wall shear stress and aneurysm area index. , 2008, Medical engineering & physics.

[22]  J. Womersley XXIV. Oscillatory motion of a viscous liquid in a thin-walled elastic tube—I: The linear approximation for long waves , 1955 .

[23]  Aichi Chien,et al.  Patient-specific hemodynamic analysis of small internal carotid artery-ophthalmic artery aneurysms. , 2009, Surgical neurology.

[24]  Dimitris Karnabatidis,et al.  Computational representation and hemodynamic characterization of in vivo acquired severe stenotic renal artery geometries using turbulence modeling. , 2008, Medical engineering & physics.

[25]  Edward V R DiBella,et al.  Flow measurement in MRI using arterial spin labeling with cumulative readout pulses--theory and validation. , 2010, Medical physics.

[26]  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.

[27]  Oscar Camara,et al.  Feasibility of estimating regional mechanical properties of cerebral aneurysms in vivo. , 2010, Medical physics.

[28]  Aichi Chien,et al.  Patient-specific flow analysis of brain aneurysms at a single location: comparison of hemodynamic characteristics in small aneurysms , 2008, Medical & Biological Engineering & Computing.

[29]  Alvaro Valencia,et al.  Blood flow dynamics and fluid–structure interaction in patient‐specific bifurcating cerebral aneurysms , 2008 .

[30]  Liliana Cesar,et al.  The mixability of angiographic contrast with arterial blood. , 2009, Medical physics.

[31]  Jürgen Weese,et al.  Clinical study of model-based blood flow quantification on cerebrovascular data , 2011, Medical Imaging.

[32]  Alejandro F. Frangi,et al.  Fast virtual deployment of self-expandable stents: Method and in vitro evaluation for intracranial aneurysmal stenting , 2012, Medical Image Anal..

[33]  Nils Daniel Forkert,et al.  AnToNIa: A Software Tool for the Hemodynamic Analysis of Cerebral Vascular Malformations Using 3D and 4D MRA Image Sequences , 2009, GI Jahrestagung.

[34]  D. Holdsworth,et al.  PIV-measured versus CFD-predicted flow dynamics in anatomically realistic cerebral aneurysm models. , 2008, Journal of biomechanical engineering.

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

[36]  N. Dyn,et al.  A butterfly subdivision scheme for surface interpolation with tension control , 1990, TOGS.

[37]  Christopher M. Putman,et al.  Qualitative comparison of intra-aneurysmal flow structures determined from conventional and virtual angiograms , 2007, SPIE Medical Imaging.

[38]  Matthias Bertram,et al.  Phantom-based experimental validation of computational fluid dynamics simulations on cerebral aneurysms. , 2010, Medical physics.

[39]  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.

[40]  Alastair J. Martin,et al.  Numerical simulations of flow in cerebral aneurysms: comparison of CFD results and in vivo MRI measurements. , 2008, Journal of biomechanical engineering.

[41]  Franz Aurenhammer,et al.  Voronoi Diagrams , 2000, Handbook of Computational Geometry.