Biomechanical Assessment of Bicuspid Aortic Valve Phenotypes: A Fluid–Structure Interaction Modelling Approach



Bicuspid aortic valve (BAV) is a congenital heart malformation with phenotypic heterogeneity. There is no prior computational study that assesses the haemodynamic and valve mechanics associated with BAV type 2 against a healthy tricuspid aortic valve (TAV) and other BAV categories.


A proof-of-concept study incorporating three-dimensional fluid-structure interaction (FSI) models with idealised geometries (one TAV and six BAVs, namely type 0 with lateral and anterior-posterior orientations, type 1 with R–L, N–R and N–L leaflet fusion and type 2) has been developed. Transient physiological boundary conditions have been applied and simulations were run using an Arbitrary Lagrangian–Eulerian formulation.


Our results showed the presence of abnormal haemodynamics in the aorta and abnormal valve mechanics: type 0 BAVs yielded the best haemodynamical and mechanical outcomes, but cusp stress distribution varied with valve orifice orientation, which can be linked to different cusp calcification location onset; type 1 BAVs gave rise to similar haemodynamics and valve mechanics, regardless of raphe position, but this position altered the location of abnormal haemodynamic features; finally, type 2 BAV constricted the majority of blood flow, exhibiting the most damaging haemodynamic and mechanical repercussions when compared to other BAV phenotypes.


The findings of this proof-of-concept work suggest that there are specific differences across haemodynamics and valve mechanics associated with BAV phenotypes, which may be critical to subsequent processes associated with their pathophysiology processes.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9


  1. 1.

    Akins, C. W., B. Travis, and A. P. Yoganathan. Energy loss for evaluating heart valve performance. J. Thorac. Cardiovasc. Surg. 136(4):820–833, 2008.

    Article  Google Scholar 

  2. 2.

    Alastruey, J., et al. On the impact of modelling assumptions in multi-scale, subject-specific models of aortic haemodynamics. J. R. Soc. Interface 13(119):20160073, 2016.

    Article  Google Scholar 

  3. 3.

    Algabri, Y. A., et al. Computational study on hemodynamic changes in patient-specific proximal neck angulation of abdominal aortic aneurysm with time-varying velocity. Australas. Phys. Eng. Sci. Med. 42(1):181–190, 2019.

    Article  Google Scholar 

  4. 4.

    Bahraseman, H. G., et al. Estimation of maximum intraventricular pressure: a three-dimensional fluid–structure interaction model. Biomed. Eng. Online 12:122, 2013.

    Article  Google Scholar 

  5. 5.

    Bahraseman, H. G., et al. Effect of exercise on blood flow through the aortic valve: a combined clinical and numerical study. Comput. Methods Biomech. Biomed. Eng. 17(16):1821–1834, 2014.

    Article  Google Scholar 

  6. 6.

    Bahraseman, H., et al. Combining numerical and clinical methods to assess aortic valve hemodynamics during exercise. Perfusion 29(4):340–350, 2014.

    Article  Google Scholar 

  7. 7.

    Barker, A. J., C. Lanning, and R. Shandas. Quantification of hemodynamic wall shear stress in patients with bicuspid aortic valve using phase-contrast MRI. Ann. Biomed. Eng. 38(3):788–800, 2010.

    Article  Google Scholar 

  8. 8.

    Barker, A. J., et al. Bicuspid aortic valve is associated with altered wall shear stress in the ascending aorta. Circ. Cardiovasc. Imaging 5(4):457–466, 2012.

    Article  Google Scholar 

  9. 9.

    Baumgartner, H., et al. Echocardiographic assessment of valve stenosis: EAE/ASE recommendations for clinical practice. Eur. J. Echocardiogr. 10(1):1–25, 2009.

    Article  Google Scholar 

  10. 10.

    Becker, W., et al. Bayesian sensitivity analysis of a model of the aortic valve. J. Biomech. 44(8):1499–1506, 2011.

    Article  Google Scholar 

  11. 11.

    Belytschko, T., J. I. Lin, and C. S. Tsay. Explicit algorithms for the nonlinear dynamics of shells. Comput. Methods Appl. Mech. Eng. 42(2):225–251, 1984.

    MATH  Article  Google Scholar 

  12. 12.

    Bock, J., et al. In vivo noninvasive 4D pressure difference mapping in the human aorta: phantom comparison and application in healthy volunteers and patients. Magn. Reson. Med. 66(4):1079–1088, 2011.

    Article  Google Scholar 

  13. 13.

    Bonomi, D., et al. Influence of the aortic valve leaflets on the fluid-dynamics in aorta in presence of a normally functioning bicuspid valve. Biomech. Model. Mechanobiol. 14(6):1349–1361, 2015.

    Article  Google Scholar 

  14. 14.

    Bonow, R. O., et al. ACC/AHA 2006 guidelines for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 48(3):e1–e148, 2006.

    Article  Google Scholar 

  15. 15.

    Braunwald, E., and R. O. Bonow. Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine (9th ed.). Philadelphia: Saunders, 2012.

    Google Scholar 

  16. 16.

    Burken, J. Determining the Effect of Congenital Bicuspid Aortic Valves on Aortic Dissection Using Computational Fluid Dynamics, in Biomedical Engineering. Iowa City: The University of Iowa, 2012.

    Google Scholar 

  17. 17.

    Butcher, J. T., and R. M. Nerem. Valvular endothelial cells and the mechanoregulation of valvular pathology. Philos. Trans. R. Soc. Lond. B 362(1484):1445–1457, 2007.

    Article  Google Scholar 

  18. 18.

    Cao, K., and P. Sucosky. Effect of bicuspid aortic valve cusp fusion on aorta wall shear stress: preliminary computational assessment and implication for aortic dilation. World J. Cardiovasc. Dis. 05(06):129–140, 2015.

    Article  Google Scholar 

  19. 19.

    Cao, K., and P. Sucosky. Computational comparison of regional stress and deformation characteristics in tricuspid and bicuspid aortic valve leaflets. Int. J. Numer. Method Biomed. Eng. 2017.

    Article  Google Scholar 

  20. 20.

    Cao, K., et al. Simulations of morphotype-dependent hemodynamics in non-dilated bicuspid aortic valve aortas. J. Biomech. 50:63–70, 2017.

    Article  Google Scholar 

  21. 21.

    Carty, G., S. Chatpun, and D. M. Espino. Modeling blood flow through intracranial aneurysms: a comparison of newtonian and non-Newtonian viscosity. J. Med. Biol. Eng. 36(3):396–409, 2016.

    Article  Google Scholar 

  22. 22.

    Chandra, S., N. M. Rajamannan, and P. Sucosky. Computational assessment of bicuspid aortic valve wall-shear stress: implications for calcific aortic valve disease. Biomech. Model. Mechanobiol. 11(7):1085–1096, 2012.

    Article  Google Scholar 

  23. 23.

    Chandran, K. B., S. E. Rittgers, and A. P. Yoganathan. Biofluid Mechanics: The Human Circulation. Boca Raton: CRC/Taylor & Francis, 2007.

    Google Scholar 

  24. 24.

    Chen, Y., and H. X. Luo. A computational study of the three-dimensional fluid–structure interaction of aortic valve. J. Fluids Struct. 80:332–349, 2018.

    Article  Google Scholar 

  25. 25.

    Choudhury, N., et al. Local mechanical and structural properties of healthy and diseased human ascending aorta tissue. Cardiovasc. Pathol. 18(2):83–91, 2009.

    MathSciNet  Article  Google Scholar 

  26. 26.

    Conti, C. A., et al. Biomechanical implications of the congenital bicuspid aortic valve: a finite element study of aortic root function from in vivo data. J. Thorac. Cardiovasc. Surg. 140(4):890–896, 2010.

    Article  Google Scholar 

  27. 27.

    Dasi, L. P., et al. Fluid mechanics of artificial heart valves. Clin. Exp. Pharmacol. Physiol. 36(2):225–237, 2009.

    Article  Google Scholar 

  28. 28.

    De Hart, J., et al. A two-dimensional fluid–structure interaction model of the aortic valve [correction of value]. J. Biomech. 33(9):1079–1088, 2000.

    Article  Google Scholar 

  29. 29.

    De Hart, J., et al. A three-dimensional computational analysis of fluid-structure interaction in the aortic valve. J. Biomech. 36(1):103–112, 2003.

    Article  Google Scholar 

  30. 30.

    Dolan, J. M., et al. High fluid shear stress and spatial shear stress gradients affect endothelial proliferation, survival, and alignment. Ann. Biomed. Eng. 39(6):1620–1631, 2011.

    Article  Google Scholar 

  31. 31.

    Doorly, D., and S. Sherwin. Geometry and flow. In: Cardiovascular Mathematics: Modeling and Simulation of the Circulatory System, edited by L. Formaggia, A. Quarteroni, and A. Venezian. New York: Springer, 2009.

    Google Scholar 

  32. 32.

    El-Hamamsy, I., A. H. Chester, and M. H. Yacoub. Cellular regulation of the structure and function of aortic valves. J. Adv. Res. 1:5–12, 2010.

    Article  Google Scholar 

  33. 33.

    Espino, D. M., D. E. Shepherd, and D. W. Hukins. Evaluation of a transient, simultaneous, arbitrary Lagrange-Euler based multi-physics method for simulating the mitral heart valve. Comput. Methods Biomech. Biomed. Eng. 17(4):450–458, 2014.

    Article  Google Scholar 

  34. 34.

    Espino, D. M., 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 51:54–60, 2015.

    MATH  Article  Google Scholar 

  35. 35.

    Faggiano, E., et al. Helical flows and asymmetry of blood jet in dilated ascending aorta with normally functioning bicuspid valve. Biomech. Model. Mechanobiol. 12(4):801–813, 2013.

    Article  Google Scholar 

  36. 36.

    Ferdous, Z., H. Jo, and R. M. Nerem. Strain magnitude-dependent calcific marker expression in valvular and vascular cells. Cells Tissues Organs 197(5):372–383, 2013.

    Article  Google Scholar 

  37. 37.

    Formaggia, L., K. Perktold, and A. Quarteroni. Basic mathematical models and motivations. In: Cardiovascular Mathematics: Modeling and Simulation of the Circulatory System, edited by L. Formaggia, A. Quarteroni, and A. Veneziani. New York: Springer, 2009.

    Google Scholar 

  38. 38.

    Gilmanov, A., and F. Sotiropoulos. Comparative hemodynamics in an aorta with bicuspid and trileaflet valves. Theoret. Comput. Fluid Dyn. 30(1–2):67–85, 2016.

    Article  Google Scholar 

  39. 39.

    Gode, S., et al. The role of the angle of the ascending aortic curvature on the development of type A aortic dissection: ascending aortic angulation and dissection. Interact. Cardiovasc. Thorac. Surg. 29(4):615–620, 2019.

    Article  Google Scholar 

  40. 40.

    Goudot, G., et al. Aortic wall elastic properties in case of bicuspid aortic valve. Front. Physiol. 10:299, 2019.

    Article  Google Scholar 

  41. 41.

    Grimard, B. H., and J. M. Larson. Aortic stenosis: diagnosis and treatment. Am. Fam. Phys. 78(6):717–724, 2008.

    Google Scholar 

  42. 42.

    Hager, A., et al. Diameters of the thoracic aorta throughout life as measured with helical computed tomography. J. Thorac. Cardiovasc. Surg. 123(6):1060–1066, 2002.

    Article  Google Scholar 

  43. 43.

    Halevi, R., et al. Fluid–structure interaction modeling of calcific aortic valve disease using patient-specific three-dimensional calcification scans. Med Biol Eng Comput 54(11):1683–1694, 2016.

    Article  Google Scholar 

  44. 44.

    Hallquist, J. LS-DYNA Keyword User’s Manual. Livermore, USA: Livermore Software Technology Corporation, LSTC, 2006.

    Google Scholar 

  45. 45.

    Hamatani, Y., et al. Pathological investigation of congenital bicuspid aortic valve stenosis, compared with atherosclerotic tricuspid aortic valve stenosis and congenital bicuspid aortic valve regurgitation. PLoS ONE 11(8):e0160208, 2016.

    Article  Google Scholar 

  46. 46.

    Heuzé, O. General form of the Mie-Grüneisen equation of state. C.R. Mec. 340:679–687, 2012.

    Article  Google Scholar 

  47. 47.

    Kim, H. J., et al. On coupling a lumped parameter heart model and a three-dimensional finite element aorta model. Ann. Biomed. Eng. 37(11):2153–2169, 2009.

    Article  Google Scholar 

  48. 48.

    Kimura, N., et al. Patient-specific assessment of hemodynamics by computational fluid dynamics in patients with bicuspid aortopathy. J. Thorac. Cardiovasc. Surg. 153(4):S52–S62, 2017.

    Article  Google Scholar 

  49. 49.

    Kouhi, E., and Y. S. Morsi. A parametric study on mathematical formulation and geometrical construction of a stentless aortic heart valve. J. Artif. Organs 16(4):425–442, 2013.

    Article  Google Scholar 

  50. 50.

    Kuan, M. Y., and D. M. Espino. Systolic fluid–structure interaction model of the congenitally bicuspid aortic valve: assessment of modelling requirements. Comput. Methods Biomech. Biomed. Eng. 18(12):1305–1320, 2015.

    Article  Google Scholar 

  51. 51.

    Lavon, K., et al. Fluid–structure interaction models of bicuspid aortic valves: the effects of nonfused cusp angles. J. Biomech. Eng. 2018.

    Article  Google Scholar 

  52. 52.

    Liu, J., J. A. Shar, and P. Sucosky. Wall shear stress directional abnormalities in BAV aortas: toward a new hemodynamic predictor of aortopathy? Front. Physiol. 9:993, 2018.

    Article  Google Scholar 

  53. 53.

    Lorenz, R., et al. 4D flow magnetic resonance imaging in bicuspid aortic valve disease demonstrates altered distribution of aortic blood flow helicity. Magn. Reson. Med. 71(4):1542–1553, 2014.

    Article  Google Scholar 

  54. 54.

    Luraghi, G., et al. Does clinical data quality affect fluid–structure interaction simulations of patient-specific stenotic aortic valve models? J. Biomech. 94:202–210, 2019.

    Article  Google Scholar 

  55. 55.

    Mahadevia, R., et al. Bicuspid aortic cusp fusion morphology alters aortic three-dimensional outflow patterns, wall shear stress, and expression of aortopathy. Circulation 129(6):673–682, 2014.

    Article  Google Scholar 

  56. 56.

    Manning, W. J. Asymptomatic aortic stenosis in the elderly: a clinical review. JAMA 310(14):1490–1497, 2013.

    Article  Google Scholar 

  57. 57.

    Markl, M., P. J. Kilner, and T. Ebbers. Comprehensive 4D velocity mapping of the heart and great vessels by cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 13:7, 2011.

    Article  Google Scholar 

  58. 58.

    Marom, G., et al. Effect of asymmetry on hemodynamics in fluid-structure interaction model of congenital bicuspid aortic valves. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012:637–640, 2012.

    Google Scholar 

  59. 59.

    McNally, A., A. Madan, and P. Sucosky. Morphotype-dependent flow characteristics in bicuspid aortic valve ascending aortas: a benchtop particle image velocimetry study. Front Physiol 8:44, 2017.

    Article  Google Scholar 

  60. 60.

    Mei, S., et al. Hemodynamics through the congenitally bicuspid aortic valve: a computational fluid dynamics comparison of opening orifice area and leaflet orientation. Perfusion 31(8):683–690, 2016.

    Article  Google Scholar 

  61. 61.

    Metzler, S. A., et al. Cyclic strain regulates pro-inflammatory protein expression in porcine aortic valve endothelial cells. J. Heart Valve Dis. 17(5):571–577, 2008; (discussion 578).

    Google Scholar 

  62. 62.

    Mirabella, L., et al. MRI-based protocol to characterize the relationship between bicuspid aortic valve morphology and hemodynamics. Ann. Biomed. Eng. 43(8):1815–1827, 2015.

    Article  Google Scholar 

  63. 63.

    Mohammadi, H., R. Cartier, and R. Mongrain. The impact of the aortic valve impairment on the distant coronary arteries hemodynamics: a fluid–structure interaction study. Med. Biol. Eng. Comput. 55(10):1859–1872, 2017.

    Article  Google Scholar 

  64. 64.

    Nishimura, R. A., et al. 2014 AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J. Am. Coll. Cardiol. 63(22):e57–e185, 2014.

    Article  Google Scholar 

  65. 65.

    Nistri, S., et al. Aortic elasticity and size in bicuspid aortic valve syndrome. Eur. Heart J. 29(4):472–479, 2008.

    Article  Google Scholar 

  66. 66.

    Oliveira, D., et al. Bicuspid aortic valve aortopathies: an hemodynamics characterization in dilated aortas. Comput. Methods Biomech. Biomed. Eng. 22(8):815–826, 2019.

    Article  Google Scholar 

  67. 67.

    Pasta, S., et al. Difference in hemodynamic and wall stress of ascending thoracic aortic aneurysms with bicuspid and tricuspid aortic valve. J. Biomech. 46(10):1729–1738, 2013.

    Article  Google Scholar 

  68. 68.

    Piatti, F., et al. 4D flow analysis of BAV-related fluid-dynamic alterations: evidences of wall shear stress alterations in absence of clinically-relevant aortic anatomical remodeling. Front. Physiol. 8:441, 2017.

    Article  Google Scholar 

  69. 69.

    Poullis, M. P., et al. Ascending aortic curvature as an independent risk factor for type A dissection, and ascending aortic aneurysm formation: a mathematical model. Eur. J. Cardiothorac. Surg. 33(6):995–1001, 2008.

    Article  Google Scholar 

  70. 70.

    Richards, K. E., et al. Influence of structural geometry on the severity of bicuspid aortic stenosis. Am. J. Physiol. Heart Circ. Physiol. 287(3):H1410–H1416, 2004.

    MathSciNet  Article  Google Scholar 

  71. 71.

    Robicsek, F., et al. The congenitally bicuspid aortic valve: how does it function? Why does it fail? Ann. Thorac. Surg. 77(1):177–185, 2004.

    Article  Google Scholar 

  72. 72.

    Rodriguez-Palomares, J. F., et al. Aortic flow patterns and wall shear stress maps by 4D-flow cardiovascular magnetic resonance in the assessment of aortic dilatation in bicuspid aortic valve disease. J. Cardiovasc. Magn. Reson. 20(1):28, 2018.

    Article  Google Scholar 

  73. 73.

    Rooprai, J., et al. Thoracic aortic aneurysm growth in bicuspid aortic valve patients: role of aortic stiffness and pulsatile hemodynamics. J. Am. Heart Assoc. 8(8):e010885, 2019.

    Article  Google Scholar 

  74. 74.

    Saikrishnan, N., et al. In vitro characterization of bicuspid aortic valve hemodynamics using particle image velocimetry. Ann. Biomed. Eng. 40(8):1760–1775, 2012.

    Article  Google Scholar 

  75. 75.

    Shewchuk, J.R. What is a good linear element? Interpolation, conditioning, and quality measures. in Eleventh International Meshing Roundtable. 2002.

  76. 76.

    Sievers, H. H., and C. Schmidtke. A classification system for the bicuspid aortic valve from 304 surgical specimens. J. Thorac. Cardiovasc. Surg. 133(5):1226–1233, 2007.

    Article  Google Scholar 

  77. 77.

    Sievers, H. H., et al. Toward individualized management of the ascending aorta in bicuspid aortic valve surgery: the role of valve phenotype in 1362 patients. J. Thorac. Cardiovasc. Surg. 148(5):2072–2080, 2014.

    Article  Google Scholar 

  78. 78.

    Simão, M., et al. Aorta ascending aneurysm analysis using CFD models towards possible anomalies. Fluids 2(2):31, 2017.

    Article  Google Scholar 

  79. 79.

    Spuhler, J. H., et al. 3D fluid–structure interaction simulation of aortic valves using a unified continuum ALE FEM model. Front. Physiol. 9:363, 2018.

    Article  Google Scholar 

  80. 80.

    Sturla, F., et al. Impact of modeling fluid-structure interaction in the computational analysis of aortic root biomechanics. Med. Eng. Phys. 35(12):1721–1730, 2013.

    Article  Google Scholar 

  81. 81.

    van Loon, R., et al. Comparison of various fluid–structure interaction methods for deformable bodies. Comput. Struct. 85(11–14):833–843, 2007.

    Article  Google Scholar 

  82. 82.

    Vergara, C., et al. Influence of bicuspid valve geometry on ascending aortic fluid dynamics: a parametric study. Artif. Organs 36(4):368–378, 2012.

    Article  Google Scholar 

  83. 83.

    Viscardi, F., et al. Comparative finite element model analysis of ascending aortic flow in bicuspid and tricuspid aortic valve. Artif. Organs 34(12):1114–1120, 2010.

    Article  Google Scholar 

  84. 84.

    Wang, S. H., L. P. Lee, and J. S. Lee. A linear relation between the compressibility and density of blood. J. Acoust. Soc. Am. 109(1):390–396, 2001.

    Article  Google Scholar 

  85. 85.

    Ward, C. Clinical significance of the bicuspid aortic valve. Heart 83(1):81–85, 2000.

    Article  Google Scholar 

  86. 86.

    Wu, W., et al. Fluid–structure interaction model of a percutaneous aortic valve: comparison with an in vitro test and feasibility study in a patient-specific case. Ann. Biomed. Eng. 44(2):590–603, 2016.

    Article  Google Scholar 

  87. 87.

    Yap, C. H., N. Saikrishnan, and A. P. Yoganathan. Experimental measurement of dynamic fluid shear stress on the ventricular surface of the aortic valve leaflet. Biomech. Model. Mechanobiol. 11(1–2):231–244, 2012.

    Article  Google Scholar 

Download references


The authors wish to thank ARUP for providing the LS-DYNA and LS-PrePost licenses for version R7.1.2.


No relevant funding.

Conflict of interest

The authors declare that they have no competing interests.

Statement Involving Human and ANIMAL Rights

No human or animal studies were carried out by the authors for this article.

Author information



Corresponding author

Correspondence to Diana M. C. de Oliveira.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Associate Editor David Steinman oversaw the review of this article.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 1584 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Oliveira, D.M.C., Abdullah, N., Green, N.C. et al. Biomechanical Assessment of Bicuspid Aortic Valve Phenotypes: A Fluid–Structure Interaction Modelling Approach. Cardiovasc Eng Tech (2020).

Download citation


  • Bicuspid aortic valve
  • Congenital malformation
  • Fluid–structure interaction
  • Multi-physics modelling