Research Papers

The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms

[+] Author and Article Information
Philipp Berg, Christoph Roloff, Samuel Voss, Gábor Janiga

University of Magdeburg,
Magdeburg 39106, Germany

Oliver Beuing

University Hospital of Magdeburg,
Magdeburg 39120, Germany

Shin-Ichiro Sugiyama

Tohoku University Graduate School of Medicine,
Sendai, Miyagi 980-8574,

Nicolas Aristokleous, Andreas S. Anayiotos

Cyprus University of Technology,
Lemesos 3036, Cyprus

Neil Ashton, Alistair Revell

The University of Manchester,
Manchester M60 1QD, UK

Neil W. Bressloff

University of Southampton,
Highfield, Southampton SO17 1BJ, UK

Alistair G. Brown

London W6 7NL, UK

Bong Jae Chung, Juan R. Cebral

George Mason University,
Fairfax, VA 22030

Gabriele Copelli

University of Parma,
Parma 43125, Italy

Wenyu Fu, Aike Qiao

Beijing University of Technology,
Beijing 100124, China

Arjan J. Geers

Universitat Pompeu Fabra,
Barcelona 08002, Spain

Simona Hodis, Dan Dragomir-Daescu

Texas A&M University,
Kingsville, TX 78363;
Mayo Clinic,
Rochester, MN 55905

Emily Nordahl, Yildirim Bora Suzen

North Dakota State University,
Fargo, ND 58102

Muhammad Owais Khan, Kristian Valen-Sendstad, David A. Steinman

University of Toronto,
Toronto, ON M5S 3G8, Canada

Kenichi Kono

Wakayama Rosai Hospital,
Wakayama 640-8505, Japan

Prahlad G. Menon, Priti G. Albal

Sun Yat-sen University—Carnegie
Mellon University Joint Institute of Engineering,
Pittsburgh, PA 15219

Otto Mierka, Raphael Münster

University of Dortmund,
Dortmund 44227, Germany

Hernán G. Morales, Odile Bonnefous

Medisys—Philips Research,
Paris 92156, France

Jan Osman, Leonid Goubergrits

Charité-Universitätsmedizin Berlin,
Berlin 13353, Germany

Jordi Pallares, Salvatore Cito

Universitat Rovira i Virgili,
Tarragona 43007, Spain

Alberto Passalacqua

Iowa State University,
Ames, IA 50011-2161

Senol Piskin, Kerem Pekkan

Koc University,
Sariyer, Istanbul 34450, Turkey

Susana Ramalho, Nelson Marques

blueCAPE Lda—CAE Solutions,
Milharado 2665-305, Portugal

Stéphane Sanchi

Préverenges 1028, Switzerland

Kristopher R. Schumacher, Jess Sturgeon

Kansas City, MO 64110

Helena Švihlová, Jaroslav Hron

Charles University,
Prague 18675, Czech Republic

Gabriel Usera, Mariana Mendina

Universidad de la República,
Montevideo 11300, Uruguay

Jianping Xiang, Hui Meng

University at Buffalo—State
University of New York,
Buffalo, NY 14203

1Corresponding author.

2Present address: P.O. Box 33 (Yliopistonkatu 4), 00014 University of Helsinki, Finland.

Manuscript received October 30, 2014; final manuscript received September 30, 2015; published online November 5, 2015. Assoc. Editor: Francis Loth.

J Biomech Eng 137(12), 121008 (Nov 05, 2015) (13 pages) Paper No: BIO-14-1543; doi: 10.1115/1.4031794 History: Received October 30, 2014; Revised September 30, 2015

With the increased availability of computational resources, the past decade has seen a rise in the use of computational fluid dynamics (CFD) for medical applications. There has been an increase in the application of CFD to attempt to predict the rupture of intracranial aneurysms, however, while many hemodynamic parameters can be obtained from these computations, to date, no consistent methodology for the prediction of the rupture has been identified. One particular challenge to CFD is that many factors contribute to its accuracy; the mesh resolution and spatial/temporal discretization can alone contribute to a variation in accuracy. This failure to identify the importance of these factors and identify a methodology for the prediction of ruptures has limited the acceptance of CFD among physicians for rupture prediction. The International CFD Rupture Challenge 2013 seeks to comment on the sensitivity of these various CFD assumptions to predict the rupture by undertaking a comparison of the rupture and blood-flow predictions from a wide range of independent participants utilizing a range of CFD approaches. Twenty-six groups from 15 countries took part in the challenge. Participants were provided with surface models of two intracranial aneurysms and asked to carry out the corresponding hemodynamics simulations, free to choose their own mesh, solver, and temporal discretization. They were requested to submit velocity and pressure predictions along the centerline and on specified planes. The first phase of the challenge, described in a separate paper, was aimed at predicting which of the two aneurysms had previously ruptured and where the rupture site was located. The second phase, described in this paper, aims to assess the variability of the solutions and the sensitivity to the modeling assumptions. Participants were free to choose boundary conditions in the first phase, whereas they were prescribed in the second phase but all other CFD modeling parameters were not prescribed. In order to compare the computational results of one representative group with experimental results, steady-flow measurements using particle image velocimetry (PIV) were carried out in a silicone model of one of the provided aneurysms. Approximately 80% of the participating groups generated similar results. Both velocity and pressure computations were in good agreement with each other for cycle-averaged and peak-systolic predictions. Most apparent “outliers” (results that stand out of the collective) were observed to have underestimated velocity levels compared to the majority of solutions, but nevertheless identified comparable flow structures. In only two cases, the results deviate by over 35% from the mean solution of all the participants. Results of steady CFD simulations of the representative group and PIV experiments were in good agreement. The study demonstrated that while a range of numerical schemes, mesh resolution, and solvers was used, similar flow predictions were observed in the majority of cases. To further validate the computational results, it is suggested that time-dependent measurements should be conducted in the future. However, it is recognized that this study does not include the biological aspects of the aneurysm, which needs to be considered to be able to more precisely identify the specific rupture risk of an intracranial aneurysm.

Copyright © 2015 by ASME
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Grahic Jump Location
Fig. 1

Representation of the investigated MCA aneurysms that were provided to all participating groups in order to select the ruptured case and to predict the rupture site—case 1 (left) and case 2 (right)

Grahic Jump Location
Fig. 2

Illustration of the measured patient-specific velocities for case 1 (black) and case 2 (gray) acquired using 2D cine PC-MRI and Doppler ultrasound, respectively

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Fig. 3

Centerlines and perpendicular planes that were used to compare the cycle-averaged and peak-systolic blood-flow predictions of all groups—case 1 (left) and case 2 (right). The centerlines are declared as A for the left and B for the right curves. The green planes are denoted as A and the blue planes as B (see online version for color).

Grahic Jump Location
Fig. 4

Measurement setup: (1) silicone phantom model, (2) microtraverse, (3) Nd:YAG-laser, (4) light sheet optics, (5) PIV camera, (6) flow meter, (7) precision valve, (8) base tank, and (9) head tank

Grahic Jump Location
Fig. 5

Centerline analysis for the cycle-averaged (left) and peak-systolic (right) velocity magnitude—top: results from case 1 centerline A and bottom: results from case 2 centerline B. Outlying predictions are colored gray using dashed lines. The green probes indicate the locations within the aneurysms, where a high similarity of the results is achieved—case 1: 1.25 cm and case 2: 1.08 cm. The mean centerline is plotted in solid red and the SD is illustrated using the gray area (see online version for color).

Grahic Jump Location
Fig. 6

Centerline analysis for the cycle-averaged pressure—left: case 1 centerline A and right: case 2 centerline B. The mean centerline is colored in red and the SD is illustrated using the gray area (see online version for color).

Grahic Jump Location
Fig. 7

Cut-plane comparison of peak-systolic velocity magnitude for case 1—top: plane A and bottom: plane B

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Fig. 8

Quantitative comparison of all 26 participation groups: SD of each group with respect to the mean plane of all groups. Exclusion of the outlying groups in the analysis would lead to an even better agreement among the majority of solutions.

Grahic Jump Location
Fig. 9

Cut-plane comparison of peak-systolic velocity magnitude for case 2—top: plane A and bottom: plane B

Grahic Jump Location
Fig. 10

Comparison of steady-state in-plane velocity magnitudes for two orthogonal planes of case 1 aneurysm. The inlet values correspond to the minimum, cycle-averaged and the maximum velocity of the patient-specific inflow measurement. The steady-state simulation results (CFD) are presented on the left, whereas the experimental data (PIV) are shown on the right.



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