Numerical simulation of blood flow in Cerebral aneurysms using two-phase model
DOI:
https://doi.org/10.32896/ajmedtech.v1n1.1-17Keywords:
Cerebral aneurysm, Two-phase blood, Wall shear stress, Oscillatory shear index, Relative residence time, Computational Fluid Dynamic, CFDAbstract
ABSTRACT: Computational fluid dynamic (CFD) simulation techniques have played an essential role in simulating and understanding the initiation, growth, and rupture of cerebral aneurysms. Hemodynamic parameters are mainly used to examine the rupture risk status of cerebral aneurysms using blood flow CFD simulation. Blood was considered as single-phase flow model with both Newtonian and non-Newtonian to predict the rupture risk analysis. However, to better understand predicting the risk of cerebral aneurysm rupture, blood requires two-phase, such as plasma and red blood cells (RBCs), also known as erythrocytes. In this study, the two-phase blood flow model was solved by the discrete phase model (DPM) with Lagrangian approach, in which blood was modeled two-phase fluid as a continuous phase plasma and particulate phase RBCs. Three patient-specific aneurysm geometries have been selected to determine wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT) with two-phase blood flow simulation. To analyze the velocity distribution inside the aneurysms, velocity streamlines and surface velocities were reported. The pulsatile blood flow simulation was performed for aneurysm geometries, where the mean inlet Reynolds number was calculated between 490 and 1370. The value of WSS, OSI, and RRT was quantified based on the Reynolds number. Reynolds number's minimum value indicates the low WSS, low OSI, and short RRT, and the maximum value of Reynolds number shows the high WSS, high OSI, and long RRT. The high WSS, high OSI, and long RRT, velocity streamlines distribution, surface velocity changes were determined with two-phase blood in aneurysm geometries, aneurysm geometry one and three are the medium and giant size saccular aneurysm may have a higher risk of rupture while aneurysm geometry two is medium size fusiform aneurysm has a lower risk of rupture. The two-phase blood flow model presents reasonable hemodynamic parameters that correlate with aneurysms rupture risk prediction.
References
N. Chalouhi, B. L. Hoh, and D. Hasan, “Review of cerebral aneurysm formation, growth, and rupture,” Stroke, vol. 44, no. 12, pp. 3613–3622, 2013.
J. Menke, J. Larsen, and K. Kallenberg, “Diagnosing cerebral aneurysms by computed tomographic angiography: meta-analysis,” Ann. Neurol., vol. 69, no. 4, pp. 646–654, 2011.
A. I. Shehata, T. Hassan, and K. M. Saqr, “Effects of Non-Newtonian Viscosity on the Hemodynamics of Cerebral Aneurysms,” Appl. Mech. Mater., vol. 819, pp. 366–370, 2016.
M. Longo et al., “Role of Hemodynamic Forces in Unruptured Intracranial Aneurysms: An Overview of a Complex Scenario,” World Neurosurg., vol. 105, pp. 632–642, 2017.
S. Omodaka et al., “Local hemodynamics at the rupture point of cerebral aneurysms determined by computational fluid dynamics analysis,” Cerebrovasc. Dis., vol. 34, no. 2, pp. 121–129, 2012.
A. Mantha, C. Karmonik, G. Benndorf, C. Strother, and R. Metcalfe, “Hemodynamics in a cerebral artery before and after the formation of an aneurysm,” Am. J. Neuroradiol., vol. 27, no. 5, pp. 1113–1118, 2006.
S. Sugiyama et al., “Computational hemodynamic analysis for the diagnosis of atherosclerotic changes in intracranial aneurysms: a proof- of-concept study using 3 cases harboring atherosclerotic and nonatherosclerotic aneurysms simultaneously,” Comput. Math. Methods Med., vol. 2016, 2016.
N. Varble, K. Kono, H. Rajabzadeh-Oghaz, and H. Meng, “Rupture Resemblance Models May Correlate to Growth Rates of Intracranial Aneurysms: Preliminary Results,” World Neurosurg., vol. 110, pp. e794– e805, 2018.
P. M. Munarriz, P. A. Gómez, I. Paredes, A. M. Castaño-Leon, S. Cepeda, and A. Lagares, “Basic Principles of Hemodynamics and Cerebral Aneurysms,” World Neurosurg., vol. 88, pp. 311–319, 2016.
Y. Wang, X. Leng, X. Zhou, W. Li, A. H. Siddiqui, and J. Xiang, “Hemodynamics in a middle cerebral artery aneurysm before its growth and fatal rupture: Case study and review of the literature,” World Neurosurg., vol. 119, pp. e395–e402, 2018.
G. Carty, S. Chatpun, and D. M. Espino, “Modeling blood flow through intracranial aneurysms: A comparison of Newtonian and non- Newtonian viscosity,” J. Med. Biol. Eng., vol. 36, no. 3, pp. 396–409, 2016
A. Valencia, H. Morales, R. Rivera, E. Bravo, and M. Galvez, “Blood flow dynamics in patient-specific cerebral aneurysm models: the relationship between wall shear stress and aneurysm area index,” Med. Eng. Phys., vol. 30, no. 3, pp. 329–340, 2008.
C. Ou, W. Huang, M. M. F. Yuen, and Y. Qian, “Hemodynamic modeling of leukocyte and erythrocyte transport and interactions in intracranial aneurysms by a multiphase approach,” J. Biomech., vol. 49, no. 14, pp. 3476–3484, 2016.
M. Dhahbi, M. Ben Chiekh, B. Gilles, J. C. Béra, and A. Jemni, “Numerical simulations of particle dynamics in a poststenotic blood vessel region within the scope of extracorporeal ultrasound stenosis treatment,” Med. Eng. Phys., vol. 34, no. 7, pp. 982–989, 2012.
S. A. J. Morsi and A. J. Alexander, “An investigation of particle trajectories in two-phase flow systems,” J. Fluid Mech., vol. 55, no. 2, pp. 193–208, 1972.
I. ANSYS, “Fluent 6.2 User’s Manual.” ANSYS, Inc. Canonsburg, PA,
T. Karino and H. L. Goldsmith, “Flow behaviour of blood cells and rigid spheres in an annular vortex,” Philos. Trans. R. Soc. London. B, Biol. Sci., vol. 279, no. 967, pp. 413–445, 1977.
J. Jung and A. Hassanein, “Three-phase CFD analytical modeling of
blood flow,” Med. Eng. Phys., vol. 30, no. 1, pp. 91–103, 2008.
K. Joisar, R. Bhoraniya, and A. Harichandan, “Numerical Analysis of Two-Phase Blood Flow in Idealized Artery with Blockage,” in Innovative Design, Analysis and Development Practices in Aerospace and Automotive Engineering (I-DAD 2018), Springer, 2019, pp. 259–267.
S. K. Yu and H. L. Goldsmith, “Behavior of model particles and blood cells at spherical obstructions in tube flow,” Microvasc. Res., vol. 6, no. 1, pp. 5–31, 1973.
P. A. Thrower, “global research conference (grace 2020),” 2020. .
K. Valen-Sendstad, K.-A. Mardal, M. Mortensen, B. A. P. Reif, and H. P. Langtangen, “Direct numerical simulation of transitional flow in a patient-specific intracranial aneurysm,” J. Biomech., vol. 44, no. 16, pp. 2826–2832, 2011.
H. Meng, V. M. Tutino, J. Xiang, and A. Siddiqui, “High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis,” Am. J. Neuroradiol., vol. 35, no. 7, pp. 1254–1262, 2014.
N. Varble et al., “Shared and distinct rupture discriminants of small and
large intracranial aneurysms,” Stroke, vol. 49, no. 4, pp. 856–864, 2018.
N. Varble, K. Kono, H. Rajabzadeh-Oghaz, and H. Meng, “Rupture resemblance models may correlate to growth rates of intracranial aneurysms: preliminary results,” World Neurosurg., vol. 110, pp. e794– e805, 2018.
J. Xiang et al., “Hemodynamic–morphologic discriminants for
intracranial aneurysm rupture,” Stroke, vol. 42, no. 1, pp. 144–152, 2011.
S. Soldozy et al., “The biophysical role of hemodynamics in the pathogenesis of cerebral aneurysm formation and rupture,” Neurosurg. Focus, vol. 47, no. 1, p. E11, 2019.
M. A. A. Sheikh, A. S. Shuib, and M. H. H. Mohyi, “A Review of Hemodynamic Parameters in Cerebral Aneurysm,” Interdiscip. Neurosurg., p. 100716, 2020.
P. Jiang et al., “Hemodynamic characteristics associated with thinner regions of intracranial aneurysm wall,” J. Clin. Neurosci., vol. 67, pp. 185– 190, 2019.
P. R. Hoskins and D. Hardman, “Three-dimensional imaging and
computational modelling for estimation of wall stresses in arteries,” Br.
J. Radiol., vol. 82, no. special_issue_1, pp. S3–S17, 2009.
J. M. Dolan, J. Kolega, and H. Meng, “High wall shear stress and spatial gradients in vascular pathology: a review,” Ann. Biomed. Eng., vol. 41, no. 7, pp. 1411–1427, 2013.
D. M. Sforza, C. M. Putman, and J. R. Cebral, “Hemodynamics of cerebral aneurysms,” Annu. Rev. Fluid Mech., vol. 41, pp. 91–107, 2009.
T. Qiu, G. Jin, H. Xing, and H. Lu, “Association between hemodynamics, morphology, and rupture risk of intracranial aneurysms: a computational fluid modeling study,” Neurol. Sci., vol. 38, no. 6, pp. 1009–1018, 2017.
S. Sugiyama et al., “Relative residence time prolongation in intracranial aneurysms: a possible association with atherosclerosis,” Neurosurgery, vol. 73, no. 5, pp. 767–776, 2013.
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