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Annals of Biomedical Engineering

, Volume 43, Issue 6, pp 1298–1309 | Cite as

An Animal-Specific FSI Model of the Abdominal Aorta in Anesthetized Mice

  • Bram TrachetEmail author
  • Joris Bols
  • Joris Degroote
  • Benedict Verhegghe
  • Nikolaos Stergiopulos
  • Jan Vierendeels
  • Patrick Segers
Article

Abstract

Recent research has revealed that angiotensin II-induced abdominal aortic aneurysm in mice can be related to medial ruptures occurring in the vicinity of abdominal side branches. Nevertheless a thorough understanding of the biomechanics near abdominal side branches in mice is lacking. In the current work we present a mouse-specific fluid–structure interaction (FSI) model of the abdominal aorta in ApoE−/− mice that incorporates in vivo stresses. The aortic geometry was based on contrast-enhanced in vivo micro-CT images, while aortic flow boundary conditions and material model parameters were based on in vivo high-frequency ultrasound. Flow waveforms predicted by FSI simulations corresponded better to in vivo measurements than those from CFD simulations. Peak-systolic principal stresses at the inner and outer aortic wall were locally increased caudal to the celiac and left lateral to the celiac and mesenteric arteries. Interestingly, these were also the locations at which a tear in the tunica media had been observed in previous work on angiotensin II-infused mice. Our preliminary results therefore suggest that local biomechanics play an important role in the pathophysiology of branch-related ruptures in angiotensin-II infused mice. More elaborate follow-up research is needed to demonstrate the role of biomechanics and mechanobiology in a longitudinal setting.

Keywords

Fluid–structure interaction Computational fluid dynamics Abdominal aorta Mouse model Mouse-specific High-frequency ultrasound Micro-CT Abdominal aortic aneurysm Dissecting aneurysm 

Notes

Acknowledgments

This research was funded by the Special Research Fund of Ghent University under Grant [BOF10/GOA/005] and by internal funds of the Laboratory of Hemodynamics and Cardiovascular Technology, EPFL. B.T. received a travel grant of the Flemish Fund for Scientific Research. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, the Hercules Foundation and the Flemish Government—department EWI.

Conflict of interest

None declared.

References

  1. 1.
    Arruda, E. M., et al. A 3-dimensionl constitutive model for the large stretch behavior of rubber elastic materials. J. Mech. Phys. Solids 41(2):389–412, 1993.CrossRefGoogle Scholar
  2. 2.
    Bersi, M. R., et al. Disparate changes in the mechanical properties of murine carotid arteries and aorta in response to chronic infusion of angiotensin-II. Int. J. Adv. Eng. Sci. Appl. Math. 4(4):228–240, 2013.PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Bols, J., et al. A computational method to assess the in vivo stresses and unloaded configuration of patient-specific blood vessels. J. Comput. Appl. Math. 246:10–17, 2013.CrossRefGoogle Scholar
  4. 4.
    Bols, J., et al. Inverse modelling of image-based patient-specific blood vessels: zero pressure geometry and in vivo stress incorporation. ESAIM—Math. Model. Num. Anal. 47(4):1059–1075, 2013.CrossRefGoogle Scholar
  5. 5.
    Bols, J., et al. Unstructured hexahedral mesh generation in complex vascular structures using a grid-based approach. Comput. Methods Biomech. Biomed. Eng., 2014, under review.Google Scholar
  6. 6.
    Broisat, A., et al. Assessing low levels of mechanical stress in aortic atherosclerotic lesions from apolipoprotein E−/− mice—brief report. Arterioscler. Thromb. Vasc. Biol. 31(5):1007–1010, 2011.PubMedCrossRefGoogle Scholar
  7. 7.
    Caro, C. G. Discovery of the role of wall shear in atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 29(2):158–161, 2009.PubMedCrossRefGoogle Scholar
  8. 8.
    Chandra, S., et al. Fluid-structure interaction modeling of abdominal aortic aneurysms: the impact of patient-specific inflow conditions and fluid/solid coupling. J. Biomech. Eng. 135(8):081001, 2013.CrossRefGoogle Scholar
  9. 9.
    Chen, G., et al. Impaired erythrocyte deformability in transgenic HO-1G143H mutant mice. Transgenic Res. 24(1):173–178, 2015.PubMedCrossRefGoogle Scholar
  10. 10.
    Cheng, C., et al. Atherosclerotic lesion size and vulnerability are determined by patterns of fluid shear stress. Circulation 113(23):2744–2753, 2006.PubMedCrossRefGoogle Scholar
  11. 11.
    Collins, M. J., et al. Mechanical properties of suprarenal and infrarenal abdominal aorta: Implications for mouse models of aneurysms. Med. Eng. Phys. 33(10):1262–1269, 2011.PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Daugherty, A., et al. Angiotensin II promotes atherosclerotic lesions and aneurysms in apolipoprotein E-deficient mice. J. Clin. Investig. 105(11):1605–1612, 2000.PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Daugherty, A., et al. Antagonism of AT2 receptors augments Angiotensin II-induced abdominal aortic aneurysms and atherosclerosis. Br. J. Pharmacol. 134(4):865–870, 2001.PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Degroote, J. Development of Algorithms for the Partitioned Simulation of Strongly Coupled Fluid-structure Interaction Problems, in Faculty of Engineering Ghent University, Ghent pp. 108–110, 2010.Google Scholar
  15. 15.
    Degroote, J., et al. Stability of a coupling technique for partitioned solvers in FSI applications. Comput. Struct. 86(23–24):2224–2234, 2008.CrossRefGoogle Scholar
  16. 16.
    Degroote, J., et al. Performance of a new partitioned procedure versus a monolithic procedure in fluid-structure interaction. Comput. Struct. 87(11–12):793–801, 2009.CrossRefGoogle Scholar
  17. 17.
    Endo, S., et al. Flow patterns and preferred sites of atherosclerotic lesions in the human aorta—II. Abdom. Aorta Biorheol 51(4):257–274, 2014.Google Scholar
  18. 18.
    Feintuch, A., et al. Hemodynamics in the mouse aortic arch as assessed by MRI, ultrasound, and numerical modeling. Am. J. Physiol. Heart Circ. Physiol. 292(2):H884–H892, 2007.PubMedCrossRefGoogle Scholar
  19. 19.
    Ford, M. D., et al. Hemodynamics of the Mouse Abdominal Aortic Aneurysm. J. Biomech. Eng. 133(12):121008, 2011.PubMedCrossRefGoogle Scholar
  20. 20.
    Gasser, T. C., et al. A rate-independent elastoplastic constitutive model for biological fiber-reinforced composites at finite strains: continuum basis, algorithmic formulation and finite element implementation. Comput. Mech. 29(4–5):340–360, 2002.CrossRefGoogle Scholar
  21. 21.
    Gavish, L., et al. Low level laser arrests abdominal aortic aneurysm by collagen matrix reinforcement in apolipoprotein E-deficient mice. Lasers Surg. Med. 44(8):664–674, 2012.PubMedCrossRefGoogle Scholar
  22. 22.
    Gavish, L., et al. Inadequate reinforcement of transmedial disruptions at branch points subtends aortic aneurysm formation in apolipoprotein-E-deficient mice. Cardiovasc. Pathol. 23(3):152–159, 2014.PubMedCrossRefGoogle Scholar
  23. 23.
    Greve, J. M., et al. Allometric scaling of wall shear stress from mice to humans: quantification using cine phase-contrast MRI and computational fluid dynamics. Am. J. Physiol. Heart Circ. Physiol. 291(4):H1700–H1708, 2006.PubMedCrossRefGoogle Scholar
  24. 24.
    Hoi, Y., et al. Correlation between local hemodynamics and lesion distribution in a novel aortic regurgitation murine model of atherosclerosis. Ann. Biomed. Eng. 39(5):1414–1422, 2011.PubMedCrossRefGoogle Scholar
  25. 25.
    Holzapfel, G. A., et al. A new constitutive framework for arterial wall mechanics and a comparative study of material models. J. Elast. 61(1–3):1–48, 2000.CrossRefGoogle Scholar
  26. 26.
    Huo, Y. L., et al. The flow field along the entire length of mouse aorta and primary branches. Ann. Biomed. Eng. 36(5):685–699, 2008.PubMedCrossRefGoogle Scholar
  27. 27.
    Ku, D. N., et al. Pulsatile flow and atherosclerosis in the human carotid bifurcation—positive correlation between plaque location and low and oscillating shear-stress. Arteriosclerosis 5(3):293–302, 1985.PubMedCrossRefGoogle Scholar
  28. 28.
    Leung, J. H., et al. Fluid structure interaction of patient specific abdominal aortic aneurysms: a comparison with solid stress models. Biomed. Eng. Online 5:33, 2006.PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Manning, M. W., et al. Abdominal aortic aneurysms: fresh insights from a novel animal model of the disease. Vasc. Med. 7(1):45–54, 2002.PubMedCrossRefGoogle Scholar
  30. 30.
    Rabben, S. I., et al. Ultrasound-based vessel wall tracking: an auto-correlation technique with RF center frequency estimation. Ultrasound Med. Biol. 28(4):507–517, 2002.PubMedCrossRefGoogle Scholar
  31. 31.
    Reddy, A. K., et al. Measurement of aortic input impedance in mice: effects of age on aortic stiffness. Am. J. Physiol.-Heart Circ. Physiol. 285:H1464–H1470, 2003.PubMedCrossRefGoogle Scholar
  32. 32.
    Reymond, P., et al. Physiological simulation of blood flow in the aorta: Comparison of hemodynamic indices as predicted by 3D FSI, 3D rigid wall and 1D models. Med. Eng. Phys. 35(6):784–791, 2013.PubMedCrossRefGoogle Scholar
  33. 33.
    Rissland, P., et al. Abdominal aortic aneurysm risk of rupture: patient-specific FSI simulations using anisotropic model. J. Biomech. Eng. 131(3):031001, 2009.PubMedCrossRefGoogle Scholar
  34. 34.
    Saraff, K., et al. Aortic dissection precedes formation of aneurysms and atherosclerosis in angiotensin II-infused, apolipoprotein E-deficient mice. Arterioscler. Thromb. Vasc. Biol. 23(9):1621–1626, 2003.PubMedCrossRefGoogle Scholar
  35. 35.
    Scotti, C. M., et al. Wall stress and flow dynamics in abdominal aortic aneurysms: finite element analysis vs. fluid-structure interaction. Comput. Methods Biomech. Biomed. Eng. 11(3):301–322, 2008.CrossRefGoogle Scholar
  36. 36.
    Thompson, S., et al. Hereditary differences in serum proteins of normal mice. Proc. Soc. Exp. Biol. Med. 87(2):315–317, 1954.PubMedCrossRefGoogle Scholar
  37. 37.
    Trachet, B., et al. An integrated framework to quantitatively link mouse-specific hemodynamics to aneurysm formation in angiotensin II-infused ApoE−/− mice. Ann. Biomed. Eng. 39(9):2430–2444, 2011.PubMedCrossRefGoogle Scholar
  38. 38.
    Trachet, B., et al. The impact of simplified boundary conditions and aortic arch inclusion on CFD simulations in the mouse aorta: a comparison with mouse-specific reference data. J. Biomech. Eng. 133(12):121006–121013, 2011.PubMedCrossRefGoogle Scholar
  39. 39.
    Trachet, B., et al. Longitudinal follow-up of ascending versus abdominal aortic aneurysm formation in angiotensin II-infused ApoE−/− mice. Artery Res. 8(1):16–23, 2014.CrossRefGoogle Scholar
  40. 40.
    Trachet, B., et al. Dissecting abdominal aortic aneurysm in ang II-infused mice: suprarenal branch ruptures and apparent luminal dilatation. Cardiovasc. Res. 105(2):213–222, 2015.PubMedCrossRefGoogle Scholar
  41. 41.
    Van Doormaal, M., et al. Inputs for subject-specific computational fluid dynamics simulation of blood flow in the mouse aorta. J. Biomech. Eng. 136(10):101008, 2014.PubMedCrossRefGoogle Scholar
  42. 42.
    Westerhof, N., et al. An artificial arterial system for pumping hearts. J. Appl. Physiol. 31:776–781, 1971.PubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2015

Authors and Affiliations

  • Bram Trachet
    • 1
    • 3
    Email author
  • Joris Bols
    • 1
    • 2
  • Joris Degroote
    • 2
  • Benedict Verhegghe
    • 1
  • Nikolaos Stergiopulos
    • 3
  • Jan Vierendeels
    • 2
  • Patrick Segers
    • 1
  1. 1.IBiTech-bioMMeda, Ghent University – iMinds Medical ITGhentBelgium
  2. 2.Department of Flow, Heat and Combustion MechanicsGhent UniversityGhentBelgium
  3. 3.Institute of BioengineeringEPFLLausanneSwitzerland

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