Computational analysis to predict the effect of pre-bifurcation stenosis on the hemodynamics of the internal and external carotid arteries

Authors

  • Hugo Bouteloup Department of Mechanical Engineering, University of Birmingham, B15 2TT, United Kingdom.
  • Johann Guimaraes de Oliveira Marinho Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Cidade Universitária Recife, Recife, Brazil.
  • Surapong chatpun Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hatyai, Songkhla 90110, Thailand. Phone: +66880891379
  • Daniel M. Espino Department of Mechanical Engineering, University of Birmingham, B15 2TT, United Kingdom.

DOI:

https://doi.org/10.15282/jmes.14.3.2020.05.0550

Keywords:

Carotid artery, computational fluid dynamics, hemodynamics, magnetic resonance, patient-specific stenosis

Abstract

This study assessed the hemodynamics of a patient-specific multiple stenosed common carotid artery including its bifurcation into internal and external carotid arteries; ICA and ECA, respectively. A three-dimensional computational model of the common carotid artery was reconstructed using a process of segmentation. Computational fluid dynamics was applied with the assumption that blood is Newtonian and incompressible under pulsatile conditions through the stenotic artery and subsequent bifurcation. Blood was modelled as ‘normal’ and ‘hyperglycaemic’. A region of large recirculation was found to form at bifurcation. The asymmetric velocity flow profile through the ICA was evident through the cardiac cycle with higher velocity at the inner walls of ICA. Hyperglycaemia was found to increase wall shear stresses on the carotid artery and reduce the blood velocity by as much as 4 times in ECA. In conclusion, hemodynamics in ICA and ECA are not equally affected by stenosis, with hyperglycaemic blood potentially providing additional complications to the clinical case. 

References

“World Health Organisation.” http://www.who.int/mediacentre/factsheets/fs310/en/ (accessed Apr. 01, 2019).

C. A. Holmstedt, T. N. Turan, and M. I. Chimowitz, “Atherosclerotic intracranial arterial stenosis: risk factors, diagnosis, and treatment,” Lancet Neurol., vol. 12, no. 11, pp. 1106–1114, 2013, doi: 10.1016/S1474-4422(13)70195-9.

A. M. Johri et al., “Carotid Ultrasound Maximum Plaque Height-A Sensitive Imaging Biomarker for the Assessment of Significant Coronary Artery Disease,” Echocardiography, vol. 33, no. 2, pp. 281–289, 2016, doi: 10.1111/echo.13007.

J. Pelz, A. Weinreich, D. Fritzsch, and D. Saur, “Quantification of Internal Carotid Artery Stenosis with 3D Ultrasound Angiography,” Ultraschall der Medizin - Eur. J. Ultrasound, vol. 36, no. 05, pp. 487–493, 2015, doi: 10.1055/s-0034-1398749.

M. Dabagh, P. Vasava, and P. Jalali, “Effects of severity and location of stenosis on the hemodynamics in human aorta and its branches,” Med. Biol. Eng. Comput., vol. 53, no. 5, pp. 463–476, 2015, doi: 10.1007/s11517-015-1253-3.

T. Blaser, K. Hofmann, T. Buerger, O. Effenberger, C.-W. Wallesch, and M. Goertler, “Risk of Stroke, Transient Ischemic Attack, and Vessel Occlusion Before Endarterectomy in Patients With Symptomatic Severe Carotid Stenosis,” Stroke, vol. 33, no. 4, pp. 1057–1062, 2002, doi: 10.1161/01.STR.0000013671.70986.39.

P. M. Rothwell and C. P. Warlow, “Low Risk of Ischemic Stroke in Patients With Reduced Internal Carotid Artery Lumen Diameter Distal to Severe Symptomatic Carotid Stenosis,” Stroke, vol. 31, no. 3, pp. 622–630, 2000, doi: 10.1161/01.STR.31.3.622.

Z.-Y. Li, F. P. P. Tan, G. Soloperto, N. B. Wood, X. Y. Xu, and J. H. Gillard, “Flow pattern analysis in a highly stenotic patient-specific carotid bifurcation model using a turbulence model,” Comput. Methods Biomech. Biomed. Engin., vol. 18, no. 10, pp. 1099–1107, 2015, doi: 10.1080/10255842.2013.873033.

A. Millon et al., “Low WSS Induces Intimal Thickening, while Large WSS Variation and Inflammation Induce Medial Thinning, in an Animal Model of Atherosclerosis,” PLoS One, vol. 10, no. 11, p. e0141880, 2015, doi: 10.1371/journal.pone.0141880.

A. Razavi, E. Shirani, and M. R. Sadeghi, “Numerical simulation of blood pulsatile flow in a stenosed carotid artery using different rheological models,” J. Biomech., vol. 44, no. 11, pp. 2021–2030, 2011, doi: 10.1016/j.jbiomech.2011.04.023.

P. Siogkas et al., “Multiscale - Patient-Specific Artery and Atherogenesis Models,” IEEE Trans. Biomed. Eng., vol. 58, no. 12, pp. 3464–3468, 2011, doi: 10.1109/TBME.2011.2164919.

B. Sui, P. Gao, Y. Lin, L. Jing, S. Sun, and H. Qin, “Hemodynamic parameters distribution of upstream, stenosis center, and downstream sides of plaques in carotid artery with different stenosis: a MRI and CFD study,” Acta radiol., vol. 56, no. 3, pp. 347–354, 2015, doi: 10.1177/0284185114526713.

Y. A. Algabri, S. Rookkapan, V. Gramigna, D. M. Espino, and S. Chatpun, “Computational study on hemodynamic changes in patient-specific proximal neck angulation of abdominal aortic aneurysm with time-varying velocity,” Australas. Phys. Eng. Sci. Med., vol. 42, no. 1, pp. 181–190, 2019, doi: 10.1007/s13246-019-00728-7.

C. Karmonik et al., “Quantitative comparison of hemodynamic parameters from steady and transient CFD simulations in cerebral aneurysms with focus on the aneurysm ostium,” J. Neurointerv. Surg., vol. 7, no. 5, pp. 367–372, 2015, doi: 10.1136/neurintsurg-2014-011182.

J. V. Lassaline and B. C. Moon, “A computational fluid dynamics simulation study of coronary blood flow affected by graft placement,” Interact. Cardiovasc. Thorac. Surg., vol. 19, no. 1, pp. 16–20, 2014, doi: 10.1093/icvts/ivu034.

S. Mei, F. S. N. de Souza Júnior, M. Y. S. Kuan, N. C. Green, and D. M. Espino, “Hemodynamics through the congenitally bicuspid aortic valve: a computational fluid dynamics comparison of opening orifice area and leaflet orientation,” Perfusion, vol. 31, no. 8, pp. 683–690, 2016, doi: 10.1177/0267659116656775.

S. M. A. Khader, B. S. Shenoy, R. Pai, S. G. Kamath, N. M. Sharif, and V. R. K. Rao, “Effect of increased severity in patient specific stenosis of common carotid artery using CFD-a case study,” World J. Model. Simul., vol. 7, pp. 113–122, 2011.

C. Öhman, D. M. Espino, T. Heinmann, M. Baleani, H. Delingette, and M. Viceconti, “Subject-specific knee joint model: Design of an experiment to validate a multi-body finite element model,” Vis. Comput., vol. 27, no. 2, pp. 153–159, 2011, doi: 10.1007/s00371-010-0537-8.

H. Bahraseman et al., “Combining numerical and clinical methods to assess aortic valve hemodynamics during exercise,” Perfusion, vol. 29, no. 4, pp. 340–350, 2014, doi: 10.1177/0267659114521103.

H. G. Bahraseman, K. Hassani, M. Navidbakhsh, D. M. Espino, Z. A. Sani, and N. Fatouraee, “Effect of exercise on blood flow through the aortic valve: a combined clinical and numerical study,” Comput. Methods Biomech. Biomed. Engin., vol. 17, no. 16, pp. 1821–1834, 2014, doi: 10.1080/10255842.2013.771179.

D. A. Steinman, J. B. Thomas, H. M. Ladak, J. S. Milner, B. K. Rutt, and J. D. Spence, “Reconstruction of carotid bifurcation hemodynamics and wall thickness using computational fluid dynamics and MRI,” Magn. Reson. Med., vol. 47, no. 1, pp. 149–159, 2002, doi: 10.1002/mrm.10025.

H. E. Burton, R. Cullinan, K. Jiang, and D. M. Espino, “Multiscale three-dimensional surface reconstruction and surface roughness of porcine left anterior descending coronary arteries,” R. Soc. Open Sci., vol. 6, no. 9, p. 190915, 2019, doi: 10.1098/rsos.190915.

N. Antonova, X. Dong, P. Tosheva, E. Kaliviotis, and I. Velcheva, “Numerical analysis of 3D blood flow and common carotid artery hemodynamics in the carotid artery bifurcation with stenosis,” Clin. Hemorheol. Microcirc., vol. 57, no. 2, pp. 159–173, 2014, doi: 10.3233/CH-141827.

D. Gallo, D. A. Steinman, and U. Morbiducci, “An Insight into the Mechanistic Role of the Common Carotid Artery on the Hemodynamics at the Carotid Bifurcation,” Ann. Biomed. Eng., vol. 43, no. 1, pp. 68–81, 2015, doi: 10.1007/s10439-014-1119-0.

Y. I. Cho, M. P. Mooney, and D. J. Cho, “Hemorheological Disorders in Diabetes Mellitus,” J. Diabetes Sci. Technol., vol. 2, no. 6, pp. 1130–1138, 2008, doi: 10.1177/193229680800200622.

D. A. Fedosov, M. Dao, G. E. Karniadakis, and S. Suresh, “Computational Biorheology of Human Blood Flow in Health and Disease,” Ann. Biomed. Eng., vol. 42, no. 2, pp. 368–387, 2014, doi: 10.1007/s10439-013-0922-3.

A. Prakobkarn, S. Chatpun, N. Ina, S. Saeheng, and N. Chantarapanich, “Influences of vascular geometry and blood property on carotid artery hemodynamics,” 2013, doi: 10.1109/BMEiCon.2013.6687731.

P. Goswami, D. K. Mandal, N. K. Manna, and S. Chakrabarti, “Numerical investigations of various aspects of plaque deposition through constricted artery,” J. Mech. Eng. Sci., vol. 13, no. 3, pp. 5306–5322, 2019, doi: 10.15282/jmes.13.3.2019.07.0433.

R. S. and W. S. C. Caro, T. Pedley, The Mechanics of the Circulation. Cambridge, UK: Cambridge University Press, 2012.

J. De Hart, G. W. M. Peters, P. J. G. Schreurs, and F. P. T. Baaijens, “A two-dimensional fluid–structure interaction model of the aortic value,” J. Biomech., vol. 33, no. 9, pp. 1079–1088, 2000, doi: 10.1016/S0021-9290(00)00068-3.

M. Y. S. Kuan and D. M. Espino, “Systolic fluid–structure interaction model of the congenitally bicuspid aortic valve: assessment of modelling requirements,” Comput. Methods Biomech. Biomed. Engin., vol. 18, no. 12, pp. 1305–1320, 2015, doi: 10.1080/10255842.2014.900663.

J. Vekasi, Z. S. Marton, G. Kesmarky, A. Cser, R. Russai, and B. Horvath, “Hemorheological alterations in patients with diabetic retinopathy,” Clin Hemorheol Microcirc, vol. 24, no. 1, pp. 59–64, 2001.

N. Filipovic and M. Kojic, “Computer simulations of blood flow with mass transport through the carotid artery bifurcation,” Theor. Appl. Mech., vol. 31, no. 1, pp. 1–33, 2004, doi: 10.2298/TAM0401001F.

M. E. Wagshul, P. K. Eide, and J. R. Madsen, “The pulsating brain: A review of experimental and clinical studies of intracranial pulsatility,” Fluids Barriers CNS, vol. 8, no. 1, p. 5, 2011, doi: 10.1186/2045-8118-8-5.

M. L. Bots, “Low diastolic blood pressure and atherosclerosis in elderly subjects. The Rotterdam study,” Arch. Intern. Med., vol. 156, no. 8, pp. 843–848, 1996, doi: 10.1001/archinte.156.8.843.

Y. Hoi, B. A. Wasserman, E. G. Lakatta, and D. A. Steinman, “Carotid Bifurcation Hemodynamics in Older Adults: Effect of Measured Versus Assumed Flow Waveform,” J. Biomech. Eng., vol. 132, no. 7, p. 071006, 2010, doi: 10.1115/1.4001265.

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, doi: 10.1007/s40846-016-0142-z.

D. Katritsis, L. Kaiktsis, A. Chaniotis, J. Pantos, E. P. Efstathopoulos, and V. Marmarelis, “Wall Shear Stress: Theoretical Considerations and Methods of Measurement,” Prog. Cardiovasc. Dis., vol. 49, no. 5, pp. 307–329, 2007, doi: 10.1016/j.pcad.2006.11.001.

A. Prakobkarn, N. Ina, S. Saeheng, and S. Chatpun, “Carotid artery stenosis pre-assessment by relationship derived from two-dimensional patient-specific model and throat velocity ratio,” World J. Model. Simul., vol. 13, pp. 3–11, 2017.

S. E. Lee, S.-W. Lee, P. F. Fischer, H. S. Bassiouny, and F. Loth, “Direct numerical simulation of transitional flow in a stenosed carotid bifurcation,” J. Biomech., vol. 41, no. 11, pp. 2551–2561, 2008, doi: 10.1016/j.jbiomech.2008.03.038.

M. Markl et al., “In Vivo Wall Shear Stress Distribution in the Carotid Artery,” Circ. Cardiovasc. Imaging, vol. 3, no. 6, pp. 647–655, 2010, doi: 10.1161/CIRCIMAGING.110.958504.

H. Gharahi, B. A. Zambrano, D. C. Zhu, J. K. DeMarco, and S. Baek, “Computational fluid dynamic simulation of human carotid artery bifurcation based on anatomy and volumetric blood flow rate measured with magnetic resonance imaging,” Int. J. Adv. Eng. Sci. Appl. Math., vol. 8, no. 1, pp. 46–60, 2016, doi: 10.1007/s12572-016-0161-6.

S. S. Varghese, S. H. Frankel, and P. F. Fischer, “Direct numerical simulation of stenotic flows. Part 2. Pulsatile flow,” J. Fluid Mech., vol. 582, p. 281, 2007, doi: 10.1017/S0022112007005836.

D. N. Ku, D. P. Giddens, C. K. Zarins, S. Glagov, and M. A. Y. June, “Pulsatile Flow and Atherosclerosis in the Human Carotid Bifurcation,” Ateriosclerosis, vol. 5, no. 3, pp. 293–302, 1985.

Q. Long, X. . Xu, K. . Ramnarine, and P. Hoskins, “Numerical investigation of physiologically realistic pulsatile flow through arterial stenosis,” J. Biomech., vol. 34, no. 10, pp. 1229–1242, 2001, doi: 10.1016/S0021-9290(01)00100-2.

H. G. Bahraseman, E. M. Languri, N. Yahyapourjalaly, and D. M. Espino, “Fluid–structure interaction modeling of aortic valve stenosis at different heart rates,” Acta Bioeng. Biomech., vol. 18, no. 3, pp. 11–20, 2016, doi: 10.5277/ABB-00429-2015-03.

H. E. Burton, J. M. Freij, and D. M. Espino, “Dynamic Viscoelasticity and Surface Properties of Porcine Left Anterior Descending Coronary Arteries,” Cardiovasc. Eng. Technol., vol. 8, no. 1, pp. 41–56, 2017, doi: 10.1007/s13239-016-0288-4.

J. M. Freij, H. E. Burton, and D. M. Espino, “Objective Uniaxial Identification of Transition Points in Non-Linear Materials: Sample Application to Porcine Coronary Arteries and the Dependency of Their Pre- and Post-Transitional Moduli with Position,” Cardiovasc. Eng. Technol., vol. 10, no. 1, pp. 61–68, 2019, doi: 10.1007/s13239-018-00395-x.

B. Chen, T. Lambrou, A. C. Offiah, P. A. Gondim Teixeira, M. Fry, and A. Todd-Pokropek, “An in vivo subject-specific 3D functional knee joint model using combined MR imaging,” Int. J. Comput. Assist. Radiol. Surg., vol. 8, no. 5, pp. 741–750, 2013, doi: 10.1007/s11548-012-0801-7.

S. Chatpun and P. Cabrales, “Effects of plasma viscosity modulation on cardiac function during moderate hemodilution,” Asian J. Transfus. Sci., vol. 4, no. 2, p. 102, 2010, doi: 10.4103/0973-6247.67034.

D. M. Espino, D. E. T. Shepherd, and D. W. L. Hukins, “Evaluation of a transient, simultaneous, arbitrary Lagrange–Euler based multi-physics method for simulating the mitral heart valve,” Comput. Methods Biomech. Biomed. Engin., vol. 17, no. 4, pp. 450–458, 2014, doi: 10.1080/10255842.2012.688818.

C. E. Lavecchia et al., “Lumbar model generator: a tool for the automated generation of a parametric scalable model of the lumbar spine,” J. R. Soc. Interface, vol. 15, no. 138, p. 20170829, 2018, doi: 10.1098/rsif.2017.0829.

D. M. Espino, D. E. T. Shepherd, and D. W. L. Hukins, “Transient large strain contact modelling: A comparison of contact techniques for simultaneous fluid–structure interaction,” Eur. J. Mech. - B/Fluids, vol. 51, pp. 54–60, 2015, doi: 10.1016/j.euromechflu.2015.01.006.

D. M. Espino, J. R. Meakin, D. W. L. Hukins, and J. E. Reid, “Stochastic Finite Element Analysis of Biological Systems: Comparison of a Simple Intervertebral Disc Model with Experimental Results,” Comput. Methods Biomech. Biomed. Engin., vol. 6, no. 4, pp. 243–248, 2003, doi: 10.1080/10255840310001606071.

C. Talayero, G. Romero, G. Pearce, and J. Wong, “Thrombectomy aspiration device geometry optimization for removal of blood clots in cerebral vessels,” J. Mech. Eng. Sci., vol. 14, no. 1, pp. 6229–6237, 2020, doi: 10.15282/jmes.14.1.2020.02.0487.

G. Romero, C. Talayero, G. Pearce, and J. Wong, “Modeling of blood clot removal with aspiration Thrombectomy devices,” J. Mech. Eng. Sci., vol. 14, no. 1, pp. 6238–6250, 2020, doi: 10.15282/jmes.14.1.2020.03.0488.

Downloads

Published

2020-09-28

How to Cite

[1]
H. Bouteloup, J. G. de O. Marinho, S. chatpun, and D. M. Espino, “Computational analysis to predict the effect of pre-bifurcation stenosis on the hemodynamics of the internal and external carotid arteries”, J. Mech. Eng. Sci., vol. 14, no. 3, pp. 7029–7039, Sep. 2020.

Similar Articles

<< < 9 10 11 12 13 14 15 16 17 18 > >> 

You may also start an advanced similarity search for this article.