Physical properties of Korean normal aortic valves based on fluid–structure interactions

  • Jeongrim Choi Department of Biomedical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
  • Jieun Park Bio-Medical Research Institute, Kyungpook National University and Hospital, Daegu 41940, Republic of Korea
  • Junghun Kim School of Computer Software, Daegu Catholic University, Gyeongsan-si, Gyeongbuk 38430, Republic of Korea
  • Jongmin Lee Department of Radiology, School of Medicine, Kyungpook National University & Hospital, Daegu 41944, Republic of Korea
Keywords: aortic valve; computational fluid dynamics; fluid-structure interaction; valve physical properties
Ariticle ID: 123

Abstract

Recently, numerical methods such as computational fluid dynamics (CFD), have been widely used in heart valve research. The CFD approach has fewer restrictions compared to clinical and experimental methods as it involves interpretations through computer calculations, and it can be used to predict and analyze fluids. For valve numerical analysis using CFD, the mechanical properties of the valve must be defined based on the physical properties. However, most of the existing heart valve numerical analysis studies have been conducted for westerners, and only a few studies on the same topic have focused on Asians. Thus, in this paper, we aim to determine the physical property parameters suitable for defining the mechanical properties of the normal aortic valves of Koreans over time. In this study, we used a fluid−structure interaction technique for the valve simulation and applied three representative valve characteristics presented in previous aortic valve simulation studies. Herein, the valve patency rates in case of simulation and multidetector computed tomography images were compared and analyzed through statistical techniques. Our results revealed that the physical properties, such as density (1050 kg/m3), Young’s modulus (2 MPa), and Poisson’s ratio (0.3), are like those of the Korean aortic valve over time. If hemodynamic evaluation of the Korean aortic valve is performed through simulation using these conditions, it can be effective in identifying the factors of heart valve disease.

References

1. Şahin B, İlgün G. Risk factors of deaths related to cardiovascular diseases in World Health Organization (WHO) member countries. Health & Social Care in the Community. 2020; 30(1): 73-80. doi: 10.1111/hsc.13156

2. Farzadfar F. Cardiovascular disease risk prediction models: challenges and perspectives. The Lancet Global Health. 2019; 7(10): e1288-e1289. doi: 10.1016/S2214-109X(19)30365-1

3. Stewart BF, Siscovick D, Lind BK, et al. Clinical factors associated with calcific aortic valve disease. Journal of the American College of Cardiology. 1997; 29(3): 630-634. doi: 10.1016/S0735-1097(96)00563-3

4. Coffey S, Roberts-Thomson R, Brown A, et al. Global epidemiology of valvular heart disease. Nature Reviews Cardiology. 2021; 18(12): 853-864. doi: 10.1038/s41569-021-00570-z

5. Gould ST, Srigunapalan S, Simmons CA, et al. Hemodynamic and Cellular Response Feedback in Calcific Aortic Valve Disease. Circulation Research. 2013; 113(2): 186-197. doi: 10.1161/circresaha.112.300154

6. Mittal R, Seo JH, Vedula V, et al. Computational modeling of cardiac hemodynamics: Current status and future outlook. Journal of Computational Physics. 2016; 305: 1065-1082. doi: 10.1016/j.jcp.2015.11.022

7. Toma M, Singh-Gryzbon S, Frankini E, et al. Clinical Impact of Computational Heart Valve Models. Materials. 2022; 15(9): 3302. doi: 10.3390/ma15093302

8. Hirschhorn M, Tchantchaleishvili V, Stevens R, et al. Fluid–structure interaction modeling in cardiovascular medicine – A systematic review 2017–2019. Medical Engineering & Physics. 2020; 78: 1-13. doi: 10.1016/j.medengphy.2020.01.008

9. Abbas SS, Nasif MS, Al-Waked R. State-of-the-art numerical fluid–structure interaction methods for aortic and mitral heart valves simulations: A review. SIMULATION. 2021; 98(1): 3-34. doi: 10.1177/00375497211023573

10. Aluru JS, Barsouk A, Saginala K, et al. Valvular heart disease epidemiology. Medical Sciences. 2022; 10(2): 32. doi: 10.3390/medsci10020032)

11. Kang DY, Ahn JM, Kim JB, et al. Inter-racial differences in patients undergoing transcatheter aortic valve implantation. Heart. 2022; 108(19): 1562-1570. doi: 10.1136/heartjnl-2021-320364

12. Dewi DEO, Hau YW, Khudzari AZM, et al. Cardiovascular Engineering. Springer Singapore; 2020.

13. Kasel AM, Cassese S, Bleiziffer S, et al. Standardized Imaging for Aortic Annular Sizing. JACC: Cardiovascular Imaging. 2013; 6(2): 249-262. doi: 10.1016/j.jcmg.2012.12.005

14. Tango AM, Salmonsmith J, Ducci A, et al. Validation and Extension of a Fluid–Structure Interaction Model of the Healthy Aortic Valve. Cardiovascular Engineering and Technology. 2018; 9(4): 739-751. doi: 10.1007/s13239-018-00391-1

15. Bavo AM, Rocatello G, Iannaccone F, et al. Fluid-Structure Interaction Simulation of Prosthetic Aortic Valves: Comparison between Immersed Boundary and Arbitrary Lagrangian-Eulerian Techniques for the Mesh Representation. Borazjani I, ed. PLOS ONE. 2016; 11(4): e0154517. doi: 10.1371/journal.pone.0154517

16. Hou Q, Liu G, Liu N, et al. Effect of Valve Height on the Opening and Closing Performance of the Aortic Valve Under Aortic Root Dilatation. Frontiers in Physiology. 2021; 12. doi: 10.3389/fphys.2021.697502

17. Benra FK, Dohmen HJ, Pei J, et al. A Comparison of One‐Way and Two‐Way Coupling Methods for Numerical Analysis of Fluid‐Structure Interactions. Swim E, ed. Journal of Applied Mathematics. 2011; 2011(1). doi: 10.1155/2011/853560

18. Abbas SS, Nasif MS, Al-Waked R. State-of-the-art numerical fluid–structure interaction methods for aortic and mitral heart valves simulations: A review. Simulation. 2021; 98(1): 3-34. doi: 10.1177/00375497211023573

19. Stupak E, Kačianauskas R, Kačeniauskas A, et al. The geometric model-based patient-specific simulations of turbulent aortic valve flows. Archives of Mechanics. 2017; 69.

20. Kousera CA, Wood NB, Seed WA, et al. A Numerical Study of Aortic Flow Stability and Comparison With In Vivo Flow Measurements. Journal of Biomechanical Engineering. 2012; 135(1). doi: 10.1115/1.4023132

21. Westerhof N, Stergiopulos N, Noble MIM, et al. Snapshots of Hemodynamics. Springer International Publishing; 2019. doi: 10.1007/978-3-319-91932-4

22. Doost SN, Zhong L, Su B, et al. Two-dimensional intraventricular flow pattern visualization using the image-based computational fluid dynamics. Computer Methods in Biomechanics and Biomedical Engineering. 2016; 20(5): 492-507. doi: 10.1080/10255842.2016.1250891

Published
2024-10-31
How to Cite
Choi, J., Park, J., Kim, J., & Lee, J. (2024). Physical properties of Korean normal aortic valves based on fluid–structure interactions. Molecular & Cellular Biomechanics, 21(1), 123. https://doi.org/10.62617/mcb.v21i1.123
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Article