Research Papers

Cerebral Blood Flow in a Healthy Circle of Willis and Two Intracranial Aneurysms: Computational Fluid Dynamics Versus Four-Dimensional Phase-Contrast Magnetic Resonance Imaging

[+] Author and Article Information
Philipp Berg

Department of Fluid Dynamics
and Technical Flows,
University of Magdeburg,
Universitaetsplatz 2,
Magdeburg 39106, Germany
e-mail: Philipp.Berg@ovgu.de

Daniel Stucht

Department of Biomedical Magnetic Resonance,
University of Magdeburg,
Leipziger Straße 44,
Magdeburg 39120, Germany
e-mail: Daniel.Stucht@ovgu.de

Gábor Janiga

Department of Fluid Dynamics
and Technical Flows,
University of Magdeburg,
Universitaetsplatz 2,
Magdeburg 39106, Germany
e-mail: Gabor.Janiga@ovgu.de

Oliver Beuing

Department of Neuroradiology,
University Hospital of Magdeburg,
Leipziger Straße 44,
Magdeburg 39120, Germany
e-mail: Oliver.Beuing@med.ovgu.de

Oliver Speck

Department of Biomedical Magnetic Resonance,
University of Magdeburg,
Leipziger Straße 44,
Magdeburg 39120, Germany
Leibniz Institute for Neurobiology,
Brenneckestraße 6,
Magdeburg 39118, Germany
e-mail: Oliver.Speck@ovgu.de

Dominique Thévenin

Department of Fluid Dynamics
and Technical Flows,
University of Magdeburg,
Universitaetsplatz 2,
Magdeburg 39106, Germany
e-mail: Dominique.Thevenin@ovgu.de

Contributed by the Bioengineering Division of ASME for publication in the JOURNAL OF BIOMECHANICAL ENGINEERING. Manuscript received May 30, 2013; final manuscript received November 21, 2013; accepted manuscript posted November 27, 2013; published online March 24, 2014. Assoc. Editor: Dalin Tang.

J Biomech Eng 136(4), 041003 (Mar 24, 2014) (9 pages) Paper No: BIO-13-1247; doi: 10.1115/1.4026108 History: Received May 30, 2013; Revised November 21, 2013; Accepted November 27, 2013

Computational fluid dynamics (CFD) opens up multiple opportunities to investigate the hemodynamics of the human vascular system. However, due to numerous assumptions the acceptance of CFD among physicians is still limited in practice and validation through comparison is mandatory. Time-dependent quantitative phase-contrast magnetic resonance imaging PC-MRI measurements in a healthy volunteer and two intracranial aneurysms were carried out at 3 and 7 Tesla. Based on the acquired images, three-dimensional (3D) models of the aneurysms were reconstructed and used for the numerical simulations. Flow information from the MR measurements were applied as boundary conditions. The four-dimensional (4D) velocity fields obtained by CFD and MRI were qualitatively as well as quantitatively compared including cut planes and vector analyses. For all cases a high similarity of the velocity patterns was observed. Additionally, the quantitative analysis revealed a good agreement between CFD and MRI. Deviations were caused by minor differences between the reconstructed vessel models and the actual lumen. The comparisons between diastole and systole indicate that relative differences between MRI and CFD are intensified with increasing velocity. The findings of this study lead to the conclusion that CFD and MRI agree well in predicting intracranial velocities when realistic geometries and boundary conditions are provided. Due to the considerably higher temporal and spatial resolution of CFD compared to MRI, complex flow patterns can be further investigated in order to evaluate their role with respect to aneurysm formation or rupture. Nevertheless, special care is required regarding the vessel reconstruction since the geometry has a major impact on the subsequent numerical results.

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Cebral, J. R., Mut, F., Weir, J., and Putman, C., 2011, “Quantitative Characterization of the Hemodynamic Environment in Ruptured and Unruptured Brain Aneurysms,” Am. J. Neuroradiol., 32(1), pp. 145–151. [CrossRef]
Zuleger, D. I., Poulikakos, D., Valavanis, A., and Kollias, S. S., 2010, “Combining Magnetic Resonance Measurements With Numerical Simulations—Extracting Blood Flow Physiology Information Relevant to the Investigation of Intracranial Aneurysms in the Circle of Willis,” Int. J. Heat Fluid, 31(6), pp. 1032–1039. [CrossRef]
Castro, M. A., Putman, C. M., and Cebral, J. R., 2006, “Computational Fluid Dynamics Modeling of Intracranial Aneurysms: Effects of Parent Artery Segmentation on Intra-Aneurysmal Hemodynamics,” Am. J. Neuroradiol., 27(8), pp. 1703–1709.
Sforza, D. M., Putman, C. M., and Cebral, J. R., 2009, “Hemodynamics of Cerebral Aneurysms,” Annu. Rev. Fluid Mech., 41, pp. 91–107. [CrossRef] [PubMed]
Meng, H., Wang, Z. J., Hoi, Y., Gao, L., Metaxa, E., Swartz, D. D., and Kolega, J., 2007, “Complex Hemodynamics at the Apex of an Arterial Bifurcation Induces Vascular Remodeling Resembling Cerebral Aneurysm Initiation,” Stroke, 38(6), pp. 1924–1931. [CrossRef] [PubMed]
Neugebauer, M., Janiga, G., Beuing, O., Skalej, M., and Preim, B., 2010, “Computer-Aided Modelling of Blood Flow for the Treatment of Cerebral Aneurysms,” Int. J. Biomed. Eng. Tech., 55, pp. 37–41.
Marzo, A., Singh, P., Larrabide, I., Radaelli, A., Coley, S., Gwilliam, M., Wilkinson, I. D., Lawford, P., Reymond, P., Patel, U., Frangi, A., and Hose, D. R., 2011, “Computational Hemodynamics in Cerebral Aneurysms: The Effects of Modeled Versus Measured Boundary Conditions,” Ann. Biomed. Eng., 39(2), pp. 884–896. [CrossRef] [PubMed]
Fiorella, D., Sadasivan, C., Woo, H. H., and Lieber, B., 2011, “Regarding `Aneurysm Rupture Following Treatment With Flow-Diverting Stents: Computational Hemodynamics Analysis of Treatment’,” Am. J. Neuroradiol., 32(5), pp. E95–97; author reply E98–100. [CrossRef]
Kallmes, D. F., 2012, “Point: CFD-Computational Fluid Dynamics or Confounding Factor Dissemination,” Am. J. Neuroradiol., 33(3), pp. 395–396. [CrossRef]
Bordás, R., Seshadhri, S., Janiga, G., Skalej, M., and Thévenin, D., 2012, “Experimental Validation of Numerical Simulations on a Cerebral Aneurysm Phantom Model,” Interv. Med. Appl. Sci., 4(4), pp. 193–205. [CrossRef] [PubMed]
Ford, M. D., Nikolov, H. N., Milner, J. S., Lownie, S. P., Demont, E. M., Kalata, W., Loth, F., Holdsworth, D. W., and Steinman, D. A., 2008, “PIV-Measured Versus CFD-Predicted Flow Dynamics in Anatomically Realistic Cerebral Aneurysm Models,” ASME J. Biomech. Eng., 130(2), p. 021015. [CrossRef]
Hoi, Y., Woodward, S. H., Kim, M., Taulbee, D. B., and Meng, H., 2006, “Validation of CFD Simulations of Cerebral Aneurysms With Implication of Geometric Variations,” ASME J. Biomech. Eng., 128(6), pp. 844–851. [CrossRef]
Rayz, V. L., Boussel, L., Acevedo-Bolton, G., Martin, A. J., Young, W. L., Lawton, M. T., Higashida, R., and Saloner, D., 2008, “Numerical Simulations of Flow in Cerebral Aneurysms: Comparison of CFD Results and In Vivo MRI Measurements,” ASME J. Biomech. Eng., 130(5), p. 051011. [CrossRef]
Hollnagel, D. I., Summers, P. E., Poulikakos, D., and Kollias, S. S., 2009, “Comparative Velocity Investigations in Cerebral Arteries and Aneurysms: 3D Phase-Contrast MR Angiography, Laser Doppler Velocimetry and Computational Fluid Dynamics,” NMR Biomed., 22(8), pp. 795–808. [CrossRef] [PubMed]
Boussel, L., Rayz, V., Martin, A., Acevedo-Bolton, G., Lawton, M. T., Higashida, R., Smith, W. S., Young, W. L., and Saloner, D., 2009, “Phase-Contrast Magnetic Resonance Imaging Measurements in Intracranial Aneurysms In Vivo of Flow Patterns, Velocity Fields, and Wall Shear Stress: Comparison With Computational Fluid Dynamics,” Magn. Reson. Med., 61(2), pp. 409–417. [CrossRef] [PubMed]
Karmonik, C., Klucznik, R., and Benndorf, G., 2008, “Blood Flow in Cerebral Aneurysms: Comparison of Phase Contrast Magnetic Resonance and Computational Fluid Dynamics—Preliminary Experience,” RoFo: Fort. Gebiete Rontgenstrahlen Nuklearmedizin, 180(3), pp. 209–215. [CrossRef]
Jiang, J., Johnson, K., Valen-Sendstad, K., Mardal, K. A., Wieben, O., and Strother, C., 2011, “Flow Characteristics in a Canine Aneurysm Model: A Comparison of 4D Accelerated Phase-Contrast MR Measurements and Computational Fluid Dynamics Simulations,” Med. Phys., 38(11), pp. 6300–6312. [CrossRef] [PubMed]
Naito, T., Miyachi, S., Matsubara, N., Isoda, H., Izumi, T., Haraguchi, K., Takahashi, I., Ishii, K., and Wakabayashi, T., 2012, “Magnetic Resonance Fluid Dynamics for Intracranial Aneurysms—Comparison With Computed Fluid Dynamics,” Acta Neurochir. (Wien), 154(6), pp. 993–1001. [CrossRef] [PubMed]
Isoda, H., Ohkura, Y., Kosugi, T., Hirano, M., Alley, M. T., Bammer, R., Pelc, N. J., Namba, H., and Sakahara, H., 2010, “Comparison of Hemodynamics of Intracranial Aneurysms Between MR Fluid Dynamics Using 3D Cine Phase-Contrast MRI and MR-Based Computational Fluid Dynamics,” Neuroradiology, 52(10), pp. 913–920. [CrossRef] [PubMed]
van Ooij, P., Schneiders, J., Marquering, H., Majoie, C. B., van Bavel, E., and Nederveen, A., 2012, “4D Phase Contrast MRI in Intracranial Aneurysms: A Comparison With Patient-Specific Computational Fluid Dynamics With Temporal and Spatial Velocity Boundary Conditions as Measured With 3D Phase Contrast MRI,” J. Cardiovasc. Magn. Reson., 14(Suppl 1), p. W3. [CrossRef]
Lee, A. T., Pike, G. B., and Pelc, N. J., 1995, “Three-Point Phase-Contrast Velocity Measurements With Increased Velocity-to-Noise Ratio,” Magn. Reson. Med., 33(1), pp. 122–126. [CrossRef] [PubMed]
Pelc, N. J., Herfkens, R. J., Shimakawa, A., and Enzmann, D. R., 1991, “Phase Contrast Cine Magnetic Resonance Imaging,” Magn. Reson. Q., 7(4), pp. 229–254. [PubMed]
Monninghoff, C., Maderwald, S., Theysohn, J. M., Kraff, O., Ladd, S. C., Ladd, M. E., Forsting, M., Quick, H. H., and Wanke, I., 2009, “Evaluation of Intracranial Aneurysms With 7 T Versus 1.5 T Time-of-Flight MR Angiography—Initial Experience,” RoFo: Fort. Gebiete Rontgenstrahlen Nuklearmedizin, 181(1), pp. 16–23. [CrossRef]
Stamm, A. C., Wright, C. L., Knopp, M. V., Schmalbrock, P., and Heverhagen, J. T., 2012, “Phase Contrast and Time-of-Flight Magnetic Resonance Angiography of the Intracerebral Arteries at 1.5, 3 and 7 T,” Magn. Reson. Imaging, 31(4), pp. 545–549. [CrossRef] [PubMed]
van Ooij, P., Zwanenburg, J. J., Visser, F., Majoie, C. B., vanBavel, E., Hendrikse, J., and Nederveen, A. J., 2013, “Quantification and Visualization of Flow in the Circle of Willis: Time-Resolved Three-Dimensional Phase Contrast MRI at 7 T Compared With 3 T,” Magn. Reson. Med., 69(3), pp. 868–876. [CrossRef] [PubMed]
Anor, T., Grinberg, L., Baek, H., Madsen, J. R., Jayaraman, M. V., and Karniadakis, G. E., 2010, “Modeling of Blood Flow in Arterial Trees,” WIREs Syst. Biol. Med., 2(5), pp. 612–623. [CrossRef]
Markl, M., Chan, F. P., Alley, M. T., Wedding, K. L., Draney, M. T., Elkins, C. J., Parker, D. W., Wicker, R., Taylor, C. A., Herfkens, R. J., and Pelc, N. J., 2003, “Time-Resolved Three-Dimensional Phase-Contrast MRI,” J. Magn. Reson. Imaging, 17(4), pp. 499–506. [CrossRef] [PubMed]
Markl, M., Harloff, A., Bley, T. A., Zaitsev, M., Jung, B., Weigang, E., Langer, M., Hennig, J., and Frydrychowicz, A., 2007, “Time-Resolved 3D MR Velocity Mapping at 3 T: Improved Navigator-Gated Assessment of Vascular Anatomy and Blood Flow,” J. Magn. Reson. Imaging, 25(4), pp. 824–831. [CrossRef] [PubMed]
Bock, J., Kreher, B. W., Hennig, J., and Markl, M., 2007, “Optimized Pre-Processing of Time-Resolved 2D and 3D Phase Contrast MRI Data,” Proceedings of the 15th Scientific Meeting. International Society for Magnetic Resonance in Medicine, Berlin, Germany, p. 3138.
Mönch, T., Gasteiger, R., Janiga, G., Theisel, H., and Preim, B., 2011, “Context-Aware Mesh Smoothing for Biomedical Applications,” Comput. Graphics, 35, pp. 755–767. [CrossRef]
Neugebauer, M., Lawonn, K., Beuing, O., and Preim, B., 2013, “Automatic Generation of Anatomic Characteristics From Cerebral Aneurysm Surface Models,” Int. J. Comput. Assist. Radiol. Surg., 8(2), pp. 279–289. [CrossRef] [PubMed]
Berg, P., Janiga, G., and Thévenin, D., 2012, “Detailed Comparison of Numerical Flow Predictions in Cerebral Aneurysms Using Different CFD Software,” Conference on Modelling Fluid Flow, J.Vad, Ed., Budapest, Hungary, pp. 128–135.
Janiga, G., Berg, P., Beuing, O., Neugebauer, M., Gasteiger, R., Preim, B., Rose, G., Skalej, M., and Thévenin, D., 2013, “Recommendations for Accurate Numerical Blood Flow Simulations of Stented Intracranial Aneurysms,” Biomed. Eng., 58(3), pp. 303–314.
Stalder, A. F., Russe, M. F., Frydrychowicz, A., Bock, J., Hennig, J., and Markl, M., 2008, “Quantitative 2D and 3D Phase Contrast MRI: Optimized Analysis of Blood Flow and Vessel Wall Parameters,” Magn. Reson. Med., 60(5), pp. 1218–1231. [CrossRef] [PubMed]
Pereira, V. M., Brina, O., Marcos Gonzales, A., Narata, A. P., Bijlenga, P., Schaller, K., Lovblad, K. O., and Ouared, R., 2013, “Evaluation of the Influence of Inlet Boundary Conditions on Computational Fluid Dynamics for Intracranial Aneurysms: A Virtual Experiment,” J. Biomech., 46(9), pp. 1531–1539. [CrossRef] [PubMed]
Rayz, V. L., and Berger, S. A., 2010, “Computational Modeling of Vascular Hemodynamics,” Computational Modeling in Biomechanics, S.De, F.Guilak, and M.Mofrad, Eds., Springer, Berlin, pp. 171–206.
Alastruey, J., Parker, K. H., Peiro, J., Byrd, S. M., and Sherwin, S. J., 2007, “Modelling the Circle of Willis to Assess the Effects of Anatomical Variations and Occlusions on Cerebral Flows,” J. Biomech., 40(8), pp. 1794–1805. [CrossRef] [PubMed]
Li, H. Y., and Shen, I. F., 2006, “Similarity Measure for Vector Field Learning,” Lect. Notes Comput. Sci., 3971, pp. 436–441.
Raschi, M., Mut, F., Byrne, G., Putman, C. M., Tateshima, S., Vinuela, F., Tanoue, T., Tanishita, K., and Cebral, J. R., 2012, “CFD and PIV Analysis of Hemodynamics in a Growing Intracranial Aneurysm,” Int. J. Numer. Method. Biomed. Eng., 28(2), pp. 214–228. [CrossRef] [PubMed]
Zaitsev, M., Dold, C., Sakas, G., Hennig, J., and Speck, O., 2006, “Magnetic Resonance Imaging of Freely Moving Objects: Prospective Real-Time Motion Correction Using an External Optical Motion Tracking System,” NeuroImage, 31(3), pp. 1038–1050. [CrossRef] [PubMed]
Bammer, R., Hope, T. A., Aksoy, M., and Alley, M. T., 2007, “Time-Resolved 3D Quantitative Flow MRI of the Major Intracranial Vessels: Initial Experience and Comparative Evaluation at 1.5 T and 3.0 T in Combination With Parallel Imaging,” Magn. Reson. Med., 57(1), pp. 127–140. [CrossRef] [PubMed]
Valencia, A., Ledermann, D., Rivera, R., Bravo, E., and Galvez, M., 2008, “Blood Flow Dynamics and Fluid-Structure Interaction in Patient-Specific Bifurcating Cerebral Aneurysms,” Int. J. Numer. Methods Fluids, 58(10), pp. 1081–1100. [CrossRef]
Ebbers, T., and Farneback, G., 2009, “Improving Computation of Cardiovascular Relative Pressure Fields From Velocity MRI,” J. Magn. Reson. Imaging, 30(1), pp. 54–61. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 1

Surface models of the investigated cases: complete Circle of Willis of a healthy volunteer (case CoW, top), anterior communicating artery aneurysm (case AcomA, bottom left), and middle cerebral artery aneurysm (case MCA, bottom right). The surface of the whole circulatory system is based on the flow measurement, whereas the aneurysms are reconstructed from the precise TOF sequences. The red circles indicate their location within the Circle of Willis.

Grahic Jump Location
Fig. 2

Representation of the methods used for qualitative and quantitative analysis, respectively: orthogonal cut planes (red and green) and arbitrary point cloud (blue)

Grahic Jump Location
Fig. 3

Qualitative comparison of peak-systolic velocity profiles at characteristic locations within the circulatory system. Numerical results (CFD) are represented as transparent shapes, whereas the flow measurements (MRI) are visualized as opaque profiles. At each location the cross-sectional velocity magnitudes are displayed (upper—MRI; lower—CFD). The internal carotid artery profiles correspond to the upper velocity legend and the lower ones are scaled different to preserve a comparable profile height.

Grahic Jump Location
Fig. 4

Comparison of peak-systolic velocity magnitude fields at selected orthogonal cut planes. Top: Anterior communicating artery aneurysm of case AcomA (tA = 0.286 s). Bottom: Middle cerebral artery aneurysm of case MCA (tM = 0.074 s). The first and third column of each case represents the CFD results, whereas the second and fourth column is associated with the MRI measurements. Peak systole was assumed for the highest flow rates through the inlet cross section during one cardiac cycle.

Grahic Jump Location
Fig. 5

Quantitative analyses of the three-dimensional measured and simulated velocity fields. Left to right column: Healthy Circle of Willis at systole and diastole, respectively; anterior communicating artery aneurysm (AcomA) at systole; middle cerebral artery aneurysm (MCA) at systole. Top to bottom row: Direct comparison of the experimental and numerical velocity magnitudes with a line indicating a perfect match; angular similarity index (ASI); magnitude similarity index (MSI), both with a maximum value of 1.




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