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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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References

Figures

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. 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. 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. 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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