A comparative study between CFD and FSI hemodynamic parameters in a patientspecific giant saccular cerebral aneurysm

Document Type : Research Article

Authors

1 Division of Biomedical Engineering, Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran; Centre de recherche du centre hospitalier de l’Universite ’de Montr ́eal (CRCHUM), Montreal, Canada; Institut de genie biomedical , Universite de Montreal, Montreal, Canada

2 Division of Biomedical Engineering, Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

3 Department of Biomedical Engineering, Division of Biomechanics, Sahand University of Technology, Tabriz, Iran

4 Chief of Services Stroke Neurology and Interventional Neuroradiology, Milad Hospital, Tehran,Iran; Head of Neuro-intervention, Nikan Hospital, Tehran, Iran

Abstract

Nowadays, biomechanical methods are useful to identify the cause and treating of diseases. One of these diseases is the cerebral aneurysm. This disease starts by the inflation of artery wall and then by rupturing, it leads to intracranial hemorrhage. Therefore, it leads to morbidity or even it is the cause of the mortality for many patients. For this reasons, it is important to anticipate the emersion, growth and the rupture of a cerebral aneurysm. Computational fluid dynamics (CFD) and 2-way fluid-structure interaction (FSI) are common methods for interrogation the rupture of aneurysms and evaluating the effective hemodynamic parameters. In this study, they were employed to obtain appropriate information of a cerebral aneurysm. A patient-specific giant aneurysm was chosen in the internal carotid artery (ICA). Mooney-Rivlin parameters were used for the solid part and a non-Newtonian Carreu model was employed in the fluid part. Important hemodynamic parameters such as wall shear stress (WSS), time average wall shear stress (TAWSS), spatial average wall shear stress (SAWSS), oscillatory shear index (OSI), and relative residence time (RRT) were discussed. In addition, these methods were then compared and the number of cycles assessed to determine the accuracy of the solutions. Both methods illustrate a similar location for the risk of a rupture related to these hemodynamic parameters but with different quantities. The novelty of this works lies at the feasibility of using the FSI and CFD methods to show the cost function in the future clinical decision-making.

Keywords

Main Subjects

References

[1] A. Aranda, A.J.M.L. Valencia, A.A.I.J. Vol, Study on cerebral aneurysms: Rupture risk prediction using geometrical parameters and wall shear stress with cfd and machine learning tools, 5 (2018).
[2] A. Aranda, A.J.J.o.M.i.M. Valencia, Biology, Computational study on the rupture risk in real cerebral aneurysms with geometrical and fluid-mechanical parameters using FSI simulations and machine learning algorithms, 19(03) (2019) 1950014.
[3] X. Bai-Nan, W. Fu-Yu, L. Lei, Z. Xiao-Jun, J.J.N.r. HaiYue, Hemodynamics model of fluid–solid interaction in internal carotid artery aneurysms, 34(1) (2011) 39-47.
[4] H.-J. Bungartz, M. Schäfer, Fluid-structure interaction: modelling, simulation, optimisation, Springer Science & Business Media, 2006.
[5] ] A. Can, R.J.N. Du, Association of hemodynamic factors with intracranial aneurysm formation and rupture: systematic review and meta-analysis, 78(4) (2016) 510520.
[6] J.R. Cebral, F. Mut, J. Weir, C.J.A.J.o.N. Putman, Quantitative characterization of the hemodynamic environment in ruptured and unruptured brain aneurysms, 32(1) (2011) 145-151.
[7] I. Chatziprodromou, A. Tricoli, D. Poulikakos, Y.J.J.o.b. Ventikos, Haemodynamics and wall remodelling of a growing cerebral aneurysm: a computational model, 40(2) (2007) 412-426.
[8] Y.I. Cho, K.R.J.B. Kensey, Effects of the non-Newtonian viscosity of blood on flows in a diseased arterial vessel. Part 1: Steady flows, 28(3-4) (1991) 241-262.
[9] V. Costalat, M. Sanchez, D. Ambard, L. Thines, N. Lonjon, F. Nicoud, H. Brunel, J.P. Lejeune, H. Dufour, P.J.J.o.b. Bouillot, Biomechanical wall properties of human intracranial aneurysms resected following surgical clipping (IRRAs Project), 44(15) (2011) 2685-2691.
[10] K.D. Dennis, D.F. Kallmes, D.J.J.o.b. DragomirDaescu, Cerebral aneurysm blood flow simulations are sensitive to basic solver settings, 57 (2017) 46-53.
[11]K. Fukazawa, F. Ishida, Y. Umeda, Y. Miura, S. Shimosaka, S. Matsushima, W. Taki, H.J.W.n. Suzuki, Using computational fluid dynamics analysis to characterize local hemodynamic features of middle cerebral artery aneurysm rupture points, 83(1) (2015) 8086.
[12] R.J. Guzman, K. Abe, C.K.J.S. Zarins, Flow-induced arterial enlargement is inhibited by suppression of nitric oxide synthase activity in vivo, 122(2) (1997) 273-280.
[13] T. Hassan, E.V. Timofeev, T. Saito, H. Shimizu, M. Ezura, T. Tominaga, A. Takahashi, K.J.A.j.o.n. Takayama, Computational replicas: anatomic reconstructions of cerebral vessels as volume numerical grids at threedimensional angiography, 25(8) (2004) 1356-1365.
[14] B. Hillen, T. Gaasbeek, H.W.J.J.o.b. Hoogstraten, A mathematical model of the flow in the posterior communicating arteries, 15(6) (1982) 441-448.
[15] H.A. Himburg, D.M. Grzybowski, A.L. Hazel, J.A. LaMack, X.-M. Li, M.H.J.A.J.o.P.-H. Friedman, C. Physiology, Spatial comparison between wall shear stress measures and porcine arterial endothelial permeability, 286(5) (2004) H1916-H1922.
[16] J.G. Isaksen, Y. Bazilevs, T. Kvamsdal, Y. Zhang, J.H. Kaspersen, K. Waterloo, B. Romner, T.J.S. Ingebrigtsen, Determination of wall tension in cerebral artery aneurysms by numerical simulation, 39(12) (2008) 31723178.
[17] J. Janela, A. Moura, A.J.J.o.C. Sequeira, a. Mathematics, A 3D non-Newtonian fluid–structure interaction model for blood flow in arteries, 234(9) (2010) 2783-2791.
[18] S. Kondo, N. Hashimoto, H. Kikuchi, F. Hazama, I. Nagata, H.J.S. Kataoka, Cerebral aneurysms arising at nonbranching sites: an experimental study, 28(2) (1997) 398-404.
[19] J. Kuroda, M. Kinoshita, H. Tanaka, T. Nishida, H. Nakamura, Y. Watanabe, N. Tomiyama, T. Fujinaka, T.J.S. Yoshimine, Cardiac cycle-related volume change in unruptured cerebral aneurysms: a detailed volume quantification study using 4-dimensional CT angiography, 43(1) (2012) 61-66.
[20] G. Lu, L. Huang, X. Zhang, S. Wang, Y. Hong, Z. Hu, D.J.A.J.o.N. Geng, Influence of hemodynamic factors on rupture of intracranial aneurysms: patient-specific 3D mirror aneurysms model computational fluid dynamics simulation, 32(7) (2011) 1255-1261.
[21] H. Meng, V. Tutino, J. Xiang, A.J.A.J.o.N. Siddiqui, High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis, 35(7) (2014) 1254-1262.
[22] Y. Murayama, S. Fujimura, T. Suzuki, H.J.N.f. Takao, Computational fluid dynamics as a risk assessment tool
for aneurysm rupture, 47(1) (2019) E12.
[23] A. Nader-Sepahi, M. Casimiro, J. Sen, N.D.J.N.
Kitchen, Is aspect ratio a reliable predictor of intracranial aneurysm rupture?, 54(6) (2004) 1343-1348.
[24] M. Ohta, S.G. Wetzel, P. Dantan, C. Bachelet, K.O. Lovblad, H. Yilmaz, P. Flaud, D.A.J.C. Rüfenacht, i. radiology, Rheological changes after stenting of a cerebral aneurysm: a finite element modeling approach, 28(6) (2005) 768-772.
[25] S. Omodaka, S.-i. Sugiyama, T. Inoue, K. Funamoto, M. Fujimura, H. Shimizu, T. Hayase, A. Takahashi, T.J.C.D. Tominaga, Local hemodynamics at the rupture point of cerebral aneurysms determined by computational fluid dynamics analysis, 34(2) (2012) 121-129.
[26] L. Pentimalli, A. Modesti, A. Vignati, E. Marchese, A. Albanese, F. Di Rocco, A. Coletti, P. Di Nardo, C. Fantini, B.J.J.o.n. Tirpakova, Role of apoptosis in intracranial aneurysm rupture, 101(6) (2004) 1018-1025.
[27] A. Robertson, P. Watton, Computational fluid dynamics in aneurysm research: critical reflections, future directions, in, Am Soc Neuroradiology, 2012.
[28] J. Schneiders, H. Marquering, R. Van den Berg, E. VanBavel, B. Velthuis, G. Rinkel, C.J.A.J.o.N. Majoie, Rupture-associated changes of cerebral aneurysm geometry: high-resolution 3D imaging before and after rupture, 35(7) (2014) 1358-1362.
[29] A. Shamloo, M.A. Nejad, M.J.J.o.t.m.b.o.b.m. Saeedi, Fluid–structure interaction simulation of a cerebral aneurysm: Effects of endovascular coiling treatment and aneurysm wall thickening, 74 (2017) 72-83.
[30] M. Shojima, M. Oshima, K. Takagi, R. Torii, M. Hayakawa, K. Katada, A. Morita, T.J.S. Kirino, Magnitude and role of wall shear stress on cerebral aneurysm: computational fluid dynamic study of 20 middle cerebral artery aneurysms, 35(11) (2004) 25002505.
[31] A. Swillens, M. De Witte, H. Nordgaard, L. Løvstakken, Van Loo, B. Trachet, J. Vierendeels, P.J.M. Segers, b. engineering, computing, Effect of the degree of LAD stenosis on “competitive flow” and flow field characteristics in LIMA-to-LAD bypass surgery, 50(8) (2012) 839-849.
[32] R. Torii, M. Oshima, T. Kobayashi, K. Takagi, T.E.J.C.M. Tezduyar, Fluid–structure interaction modeling of aneurysmal conditions with high and normal blood pressures, 38(4) (2006) 482-490.
[33] A. Valencia, F.J.I.C.i.H. Baeza, M. Transfer, Numerical simulation of fluid–structure interaction in stenotic arteries considering two layer nonlinear anisotropic structural model, 36(2) (2009) 137-142.
[34] A. Valencia, P. Burdiles, M. Ignat, J. Mura, E. Bravo, R. Rivera, J.J.C. Sordo, m.m.i. medicine, Fluid structural analysis of human cerebral aneurysm using their own wall mechanical properties, 2013 (2013).
[35] A. Valencia, D. Ledermann, R. Rivera, E. Bravo, M.J.I.J.f.n.m.i.f. Galvez, Blood flow dynamics and fluid–structure interaction in patient-specific bifurcating cerebral aneurysms, 58(10) (2008) 1081-1100.
[36] Y. Wang, X. Leng, X. Zhou, W. Li, A.H. Siddiqui, J.J.W.n. Xiang, Hemodynamics in a middle cerebral artery aneurysm before its growth and fatal rupture: Case study and review of the literature, 119 (2018) e395-e402.
[37] G.K. Wong, W.J.J.o.C.N. Poon, Current status of computational fluid dynamics for cerebral aneurysms:
the clinician’s perspective, 18(10) (2011) 1285-1288.
[38] J. Xiang, S.K. Natarajan, M. Tremmel, D. Ma, J. Mocco, L.N. Hopkins, A.H. Siddiqui, E.I. Levy, H.J.S. Meng, Hemodynamic–morphologic discriminants for  intracranial aneurysm rupture, 42(1) (2011) 144-152.
[39] A. Ziegler, U.J.B.J.J.o.M.M.i.B. Grömping, The generalised estimating equations: A comparison of procedures available in commercial statistical software packages, 40(3) (1998) 245-260.
[40] K.M.J.P.o.t.I.o.M.E. Saqr, Part H: Journal of Engineering in Medicine, Computational fluid dynamics simulations of cerebral aneurysm using Newtonian, power-law and quasi-mechanistic blood viscosity models, 234(7) (2020) 711-719.
[41] M. Saeedi, A. Shamloo, A.J.J.o.v.r. Mohammadi, FluidStructure Interaction Simulation of Blood Flow and Cerebral Aneurysm: Effect of Partly Blocked Vessel, 56(6) (2019) 296-307.
AUT Journal of Modeling and Simulation
Volume 53, Issue 1
June 2021
Pages 23-38

Files

Share

How to cite

Statistics