Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Proceedings of the International Conference on Modern Medicine and Global Health (ICMMGH 2023)

Series Vol. 6 , 03 August 2023


Open Access | Article

The influence of vortices on hemodynamics in blood vessels

Yiming Weng * 1
1 McMaster University

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 6, 172-180
Published 03 August 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yiming Weng. The influence of vortices on hemodynamics in blood vessels. TNS (2023) Vol. 6: 172-180. DOI: 10.54254/2753-8818/6/20230216.

Abstract

Blood flow in vessels is affected by several factors like vessel shape, blood thickness, and heart function. Swirling patterns of flow, called vortices, are often seen in blood vessels and can affect how blood flows. This study aims to understand how vortices affect blood flow and the reasons behind these changes. Different instruments, like particle image velocimetry (PIV), computational fluid dynamics (CFD), and magnetic resonance imaging (MRI), were used to measure and analyze blood flow. CFD simulations were done using realistic blood vessel models to study how vortices form and how they affect blood velocity and pressure. The results show that vortices can cause significant changes in blood velocity and pressure, which can lead to changes in blood flow. The increased wall shear stress may contribute to the development of heart disease. This research highlights the importance of considering the impact of vortices on blood flow dynamics when designing and assessing cardiovascular devices and treatments.

Keywords

hemodynamic, vortices, computational fluid dynamics, velocimetry, magnetic resonance imaging

References

1. Dabiri, J., & Morteza, G. The role of optimal vortex formation in biological fluid transport. The Royal Society Publishing. (February 20, 2023). https://doi.org/10.1098/rspb.2005.3109

2. Kabir, M., M., Alam, F., & Uddin, A. (2018, April). A Numerical Study on the Effects of Reynolds Number on Blood Flow with Spiral Velocity Through Regular Arterial Stenosis. Researchgate. (February 19, 2023) https://www.researchgate.net/publication/326441047_A_Numerical_Study_on_the_Effects_of_Reynolds_Number_on_Blood_Flow_with_Spiral_Velocity_Through_Regular_Arterial_Stenosis

3. Derivation of Reynolds Equation from Navier-Stokes Equations. (2021, November 1). Tribnet About Tribology. Retrieved February 20, 2023, from https://www.tribonet.org/wiki/reynolds-equation-derivation-from-navier-stokes-equations/

4. Truffer, F., Geiser, M., Chappelet, M., Strese, H., Maitre, G., Amoos, S., Aptel, F., & Chiquet, C. (2020). Absolute retinal blood flowmeter using a laser Doppler velocimeter combined with adaptive optics. Journal of Biomedical Optics, 25(11). https://doi.org/10.1117/1.jbo.25.11.115002

5. Tango AM, Salmonsmith J, Ducci A, Burriesci G. Validation and extension of a fluid- structure interaction model of the healthy aortic valve. Cardiovasc Eng Technol 2018; 9:739–51. https://doi.org/10.1007/s13239-018-00391-1.

6. Pauls, J. P., Bartnikowski, N., Jansen, S., Lim, E., & Dasse, K. A. (2018b). Preclinical evaluation. Chapter 13 - Preclinical Evaluation. https://doi.org/10.1016/b978-0-12-810491-0.00013-8

7. Optica Publishing Group. (n.d.). https://opg.optica.org/boe/fulltext.cfm?uri=boe-10-11-5862&id=422522

8. Truffer, F., Geiser, M., Chappelet, M., Strese, H., Maitre, G., Amoos, S., Aptel, F., & Chiquet, C. (2020). Absolute retinal blood flowmeter using a laser Doppler velocimeter combined with adaptive optics. Journal of Biomedical Optics, 25(11). https://doi.org/10.1117/1.jbo.25.11.115002

9. Westerweel, J., Elsinga, G. E., & Adrian, R. J. (2013). Particle Image Velocimetry for Complex and Turbulent Flows. Annual Review of Fluid Mechanics, 45(1), 409–436. https://doi.org/10.1146/annurev-fluid-120710-101204

10. Kamada, H., Nakamura, M., Ota, H., Ejima, K., & Takase, K. (2022). Blood flow analysis with computational fluid dynamics and 4D-flow MRI for vascular diseases. Journal of Cardiology, 80(5), 386–396. https://doi.org/10.1016/j.jjcc.2022.05.007

11. Simon, H.A., Ge L., Borazjani, I., Sotiropoulos, F., Yoganathan, A.P. Simulation of the three- dimensional hinge flow fields of a bileaflet mechanical heart valve under aortic con- ditions. Ann Biomed Eng 2010;38:841–53. https://doi.org/10.1007/s10439-009- 9857-0.

12. Sodhani, D., Reese, S., Aksenov, A., Soğanci, S., Jockenhövel, S., Mela, P., et al. Fluid- structure interaction simulation of artificial textile reinforced aortic heart valve: validation with an in-vitro test. J Biomech 2018;78:52–69. https://doi.org/10. 1016/j.jbiomech.2018.07.018.

13. Tango, A.M., Salmonsmith, J., Ducci, A., Burriesci, G. Validation and extension of a fluid- structure interaction model of the healthy aortic valve. Cardiovasc Eng Technol 2018;9:739–51. https://doi.org/10.1007/s13239-018-00391-1.

14. Cheng Z., Wood N.B., Gibbs R.G., Xu X.Y. Geometric and flow features of type B aortic dissection: initial findings and comparison of medically treated and stented cases. Ann Biomed Eng 2015;43:177–89. https://doi.org/10.1007/s10439-014-1075-8.

15. Bock J., Frydrychowicz A., Stalder A.F., Bley T.A., Burkhardt H, Hennig J, et al. 4D phase contrast MRI at 3 T: effect of standard and blood-pool contrast agents on SNR, PC- MRA, and blood flow visualization. Magn Reson Med 2010;63:330–8. https://doi. org/10.1002/mrm.22199.

16. Miyazaki S., Itatani K., Furusawa T., Nishino T., Sugiyama M., Takehara Y., et al. Valida- tion of numerical simulation methods in aortic arch using 4D Flow MRI. Heart Vessels 2017;32:1032–44. https://doi.org/10.1007/s00380-017-0979-2.

17. Miyazaki M., Lee VS. Nonenhanced MR angiography. Radiology, 2008; 248:20–43. https://doi.org/10.1148/radiol.2481071497.

18. Perera R., Isoda H., Ishiguro K., Mizuno T., Takehara Y., Terada M., et al. alAssessing the risk of intracranial aneurysm rupture using morphological and hemodynamic bio- markers evaluated from magnetic resonance fluid dynamics and computational fluid dynamics. Magn Reson Med Sci 2020;19:333–44. https://doi.org/10.2463/ mrms.mp.2019-0107

19. Van Ooij P., Potters W.V., Guédon A., Schneiders J.J., Marquering H.A., Majoie C.B., et al. Wall shear stress estimated with phase contrast MRI in an in vitro and in vivo in- tracranial aneurysm. J Magn Reson Imaging 2013;38:876–84. https://doi.org/10. 1002/jmri.24051.

20. Karmonik C., Müller-Eschner M, Partovi S, Geisbüsch P, Ganten MK, Bismuth J, et al. Computational fluid dynamics investigation of chronic aortic dissection hemody- namics versus normal aorta. Vasc Endovascular Surg 2013;47:625–31. https://doi. org/10.1177/1538574413503561.

21. Cheng Z., Wood N.B., Gibbs R.G., Xu X.Y. Geometric and flow features of type B aortic dissection: initial findings and comparison of medically treated and stented cases. Ann Biomed Eng 2015;43:177–89. https://doi.org/10.1007/s10439-014-1075-8.

22. Menichini C., Cheng Z., Gibbs R.G., Xu X.Y. Predicting false lumen thrombosis in patient-specific models of aortic dissection. J R Soc Interface 2016; 13:20160759. https://doi.org/10.1098/rsif.2016.0759.

23. Menichini C., Xu XY. Mathematical modeling of thrombus formation in idealized models of aortic dissection: initial findings and potential applications. J Math Biol 2016; 73:1205–26. https://doi.org/10.1007/s00285-016-0986-4.

24. Xu H., Piccinelli M., Leshnower B.G., Lefieux A., Taylor W.R., Veneziani A. Coupled morphological-hemodynamic computational analysis of type B aortic dissection: a longitudinal study. Ann Biomed Eng 2018; 46:927–39. https://doi.org/10.1007/ s10439-018-2012-z.

25. Osswald A., Karmonik C., Anderson J.R., Rengier F., Karck M., Engelke J., et al. Elevated wall shear stress in aortic type B dissection may relate to retrograde aortic type a dissection: a computational fluid dynamics pilot study. Eur J Vasc Endovasc Surg 2017; 54:324–30. https://doi.org/10.1016/j.ejvs.2017.06.012.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the International Conference on Modern Medicine and Global Health (ICMMGH 2023)
ISBN (Print)
978-1-915371-65-2
ISBN (Online)
978-1-915371-66-9
Published Date
03 August 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/6/20230216
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated