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SIMULATION ANALYSIS OF BLOOD FLOW IN ARTERIES OF THE HUMAN ARM

Corresponding author: Wu Quanyu, Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, 1801 Zhongwu Avenue, Changzhou 213001, Jiangsu, P. R. China. Tel: (+86) 519-86953225.

Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, P. R. China

E-mail Address: [email protected]

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Liu Xiaojie

Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, P. R. China

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Pan Lingjiao

Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, P. R. China

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Tao Weige

Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, P. R. China

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and

Qian Chunqi

Department of Radiology, Michigan State University, East Lansing, MI 48864, USA

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https://doi.org/10.4015/S1016237217500314Cited by:6

Abstract

Arteries in the upper limb play important roles in the circulation system of the human body. In particular, the radial artery has received considerable attention in traditional Chinese medicine for thousands of years. Here, a 3D model for the arm arteries has been created uncomplicated, in a Chinese adult’s left hand, from the magnetic resonance imaging data, using professional modeling software to restore the basic structure of the arm artery in human body, before being imported to Ansys software for simulation. Blood model has been only simulated, and using the blood density of constant parameter and viscosity using the Carreau fluid model, and using viscous-laminar model of Fluent to obtain the velocity profile, static pressure and shear stress in the brachial, interosseous, ulnar, radial and palmar arch arteries. In particular, the brachial and bifurcations have the high pressure and velocity profiles. The simulation results obtained here are also validated by those published in the literature and proved the ulnar artery prevails over the radial artery as a blood supplier to the vessels in the wrist and hand.

Keywords:

References

1. Alastruey J, Xiao N, Fok H, Schaeffter T, Figueroa CA, On the impact of modelling assumptions in multi-scale, subject-specific models of aortic haemodynamics, J R Soc Interf 13:1–17, 2016. Crossref, ISI, Google Scholar
2. Mittal R, Seo JH, Vedula V, Choi YJ, Liu H, Huang HH, Jain S, Younes L, Abraham T, George RT, Computational modeling of cardiac hemodynamics: Current status and future outlook, J Comput Phys 305 :1065–1082, 2016. Crossref, ISI, Google Scholar
3. Ramachandra AB, Kahn AM, Marsden AL, Patient-specific simulations reveal significant differences in mechanical stimuli in venous and arterial coronary grafts, J Cardiovasc Transl Res 9 :279–290, 2016. Crossref, ISI, Google Scholar
4. Leng X, Scalzo F, Fong AK, Johnson M, Ip HL, Soo Y, Leung T, Liu L, Feldmann E, Wong KS, Liebeskind DS, Computational fluid dynamics of computed tomography angiography to detect the hemodynamic impact of intracranial atherosclerotic stenosis, Neurovasc Imaging 1:1–7, 2015. Crossref, Google Scholar
5. Saho T, Onishi H, Quantitative comparison of hemodynamics in simulated and 3d angiography models of cerebral aneurysms by use of computational fluid dynamics, Radiol Phys Technol 8 :258–265, 2015. Crossref, ISI, Google Scholar
6. Rispoli VC, Nielsen JF, Nayak KS, Carvalho JL, Computational fluid dynamics simulations of blood flow regularized by 3d phase contrast mri, Biomed Eng Online 14 :110, 2015. Crossref, ISI, Google Scholar
7. Gharahi H, Zambrano BA, Zhu DC, DeMarco JK, Baek S, 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 8 :40–60, 2016. Crossref, ISI, Google Scholar
8. Potters WV, Cibis M, Marquering HA, vanBavel E, Gijsen F, Wentzel JJ, Nederveen AJ, 4d mri-based wall shear stress quantification in the carotid bifurcation: A validation study in volunteers using computational fluid dynamics, J Cardiovasc Magn Reson 16 :P162, 2014. Crossref, Google Scholar
9. Suito H, Takizawa K, Huynh VQH, Sze D, Ueda T, Fsi analysis of the blood flow and geometrical characteristics in the thoracic aorta, Comput Mech 54 :1035–1045, 2014. Crossref, ISI, Google Scholar
10. Al-Rawi M, Al-Jumaily AM, Assessing abdominal aorta narrowing using computational fluid dynamics, Med Biol Eng Comput 54 :843–853, 2016. Crossref, ISI, Google Scholar
11. Nestola MGC, Gizzi A, Cherubini C, Filippi S, Three-band decomposition analysis in multiscale fsi models of abdominal aortic aneurysms, Int J Mod Phys C 27 :1650017, 2016. Link, ISI, Google Scholar
12. Celestin C, Guillot M, Ross-Ascuitto N, Ascuitto R, Computational fluid dynamics characterization of blood flow in central aorta to pulmonary artery connections: Importance of shunt angulation as a determinant of shear stress-induced thrombosis, Pediatr Cardiol 36 :600–615, 2015. Crossref, ISI, Google Scholar
13. Ballarin F, Faggiano E, Ippolito S, Manzoni A, Quarteroni A, Rozza G, Scrofani R, Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD–Galerkin method and a vascular shape parametrization, J Comput Phys 315 :609–628, 2016. Crossref, ISI, Google Scholar
14. Qureshi MU, Vaughan GD, Sainsbury C, Johnson M, Peskin CS, Olufsen MS, Hill NA, Numerical simulation of blood flow and pressure drop in the pulmonary arterial and venous circulation, Biomech Model Mechanobiol 13 :1137–1154, 2014. Crossref, ISI, Google Scholar
15. Watanabe SM, Blanco PJ, Feijóo RA, Mathematical model of blood flow in an anatomically detailed arterial network of the arm, ESAIM: Math Model Numer Anal 47 :961–985, 2013. Crossref, ISI, Google Scholar
16. Choudhari P, Panse MS, Finite element modeling and simulation of arteries in the human arm to study the aortic pulse wave propagation, Proc Comp Sci 93 :721–727, 2016. Crossref, Google Scholar
17. Kim K, Kim JU, Beak HM, Kim SK, Modeling of the artery tree in the human upper extremity and numerical simulation of blood flow in the artery tree, Trans Kor Soc Mech Engrs B 40 :221–226, 2016. Crossref, Google Scholar
18. Wu Q-Y, Ma Z-C, Sun Y-N, Noninvasive power spectrum analysis of radial pressure waveform for assessment of vascular system, J Mech Med Biol 12 :1250021, 2012. Link, ISI, Google Scholar
19. Hashimoto J, Ito S, Pulse pressure amplification, arterial stiffness, and peripheral wave reflection determine pulsatile flow waveform of the femoral artery, Hypertension 56 :926–933, 2010. Crossref, ISI, Google Scholar
20. Avolio AP, Van Bortel LM, Boutouyrie P, Cockcroft JR, McEniery CM, Protogerou AD, Roman MJ, Safar ME, Segers P, Smulyan H, Role of pulse pressure amplification in arterial hypertension: Experts’ opinion and review of the data, Hypertension 54 :375–383, 2009. Crossref, ISI, Google Scholar
21. Zhang YL, Zheng YY, Ma ZC, Sun YN, Radial pulse transit time is an index of arterial stiffness, Hypertens Res 34 :884–887, 2011. Crossref, ISI, Google Scholar

Published: 14 August 2017

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Vol. 29, No. 04

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Accepted 18 July 2017

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SIMULATION ANALYSIS OF BLOOD FLOW IN ARTERIES OF THE HUMAN ARM

Abstract

References

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