Advertisement

Annals of Biomedical Engineering

, Volume 41, Issue 7, pp 1492–1504 | Cite as

Investigating the Influence of Haemodynamic Stimuli on Intracranial Aneurysm Inception

  • Haoyu Chen
  • Alisa Selimovic
  • Harry Thompson
  • Alessandro Chiarini
  • Justin Penrose
  • Yiannis Ventikos
  • Paul N. WattonEmail author
Article

Abstract

We propose a novel method to reconstruct the hypothetical geometry of the healthy vasculature prior to intracranial aneurysm (IA) formation: a Frenet frame is calculated along the skeletonization of the arterial geometry; upstream and downstream boundaries of the aneurysmal segment are expressed in terms of the local Frenet frame basis vectors; the hypothetical healthy geometry is then reconstructed by propagating a closed curve along the skeleton using the local Frenet frames so that the upstream boundary is smoothly morphed into the downstream boundary. This methodology takes into account the tortuosity of the arterial vasculature and requires minimal user subjectivity. The method is applied to 22 clinical cases depicting IAs. Computational fluid dynamic simulations of the vasculature without IA are performed and the haemodynamic stimuli in the location of IA formation are examined. We observe that locally elevated wall shear stress (WSS) and gradient oscillatory number (GON) are highly correlated (20/22 for WSS and 19/22 for GON) with regions susceptible to sidewall IA formation whilst haemodynamic indices associated with the oscillation of the WSS vectors have much lower correlations.

Keywords

Intracranial aneurysm Inception Haemodynamic indices Vessel reconstruction methods WSS WSSG OSI AFI GON Mechanobiology 

Notes

Acknowledgments

Haoyu Chen is funded by the Qualcomm scholarship provided by Qualcomm Inc. (Qualcomm Inc., San Diego, CA). Alisa Selimovic is funded by the Robert Menzies Memorial Scholarship in Engineering. Paul N. Watton holds a University Research Lectureship funded by the Centre of Excellence in Personalized Healthcare (funded by the Wellcome Trust and EPSRC, grant number WT 088877/Z/09/Z). This support is gratefully acknowledged.

References

  1. 1.
    Aird, W. C. Spatial and temporal dynamics of the endothelium. J. Thrombosis Haemost. 3(7):1392–1406, 2005.CrossRefGoogle Scholar
  2. 2.
    Alnaes, M. S., J. Isaksen, K. A. Mardal, B. Romner, M. K. Morgan, T. L. Ingebrigtsen. Computation of hemodynamics in the circle of willis. Stroke 38:2500–2505, 2007.PubMedCrossRefGoogle Scholar
  3. 3.
    Aoki, T., and M. Nishimura. Molecular mechanism of cerebral aneurysm formation focusing on NF-κB as a key mediator of inflammation. J. Biorheol. 24(1):16–21, 2010.CrossRefGoogle Scholar
  4. 4.
    Augst, A. D., B. Ariff, S. A. G. McG Thom, X. Y. Xu, and A. D. Hughes. Analysis of complex flow and the relationship between blood pressure, wall shear stress, and intima-media thickness in the human carotid artery. Am. J. Physiol. Heart Circ. Physiol. 293:H1031–H1037, 2007.PubMedCrossRefGoogle Scholar
  5. 5.
    Baek, H., M. V. Jayaraman, and G. E. Karniadakis. Wall shear stress and pressure distribution on aneurysms and infundiblae in the posterior communicating artery bifurcation. Ann. Biomed. Eng. 37:2469–2487, 2009.PubMedCrossRefGoogle Scholar
  6. 6.
    Brisman, J. L., J. K. Song, and D. W. Newell. Cerebral aneurysms. N. Engl. J. Med. 355:928–939, 2006.PubMedCrossRefGoogle Scholar
  7. 7.
    Cebral, J. R., M. A. Castro, O. Soto, R. Lohner, and N. Alperin. Blood-flow models of the circle of willis from magnetic resonance data. J. Eng. Math. 47:369–386, 2003.CrossRefGoogle Scholar
  8. 8.
    Cebral, J. R., and H. Meng. Counterpoint: realizing the clinical utility of computational fluid dynamics–closing the gap. AJNR—Am. J. Neuroradiol. 33(3):396–398, 2012.PubMedCrossRefGoogle Scholar
  9. 9.
    Cebral, J. R., M. C. Putman, M. T. Alley, T. Hope, R. Bammer, and F. Calamante. Hemodynamics in normal cerebral arteries: qualitative comparison of 4d phase-contrast magnetic resonance and image-based computational fluid dynamics. J. Eng. Math. 64:367–378, 2009.PubMedCrossRefGoogle Scholar
  10. 10.
    Cheng, C. P., D. Parker, and C. A. Taylor. Quantification of wall shear stress in large blood vessels using Lagrangian interpolation functions with cine phase-contrast magnetic resonance imaging. Ann. Biomed. Eng. 30(8):1020–1032, 2002.PubMedCrossRefGoogle Scholar
  11. 11.
    Doenitz, C., K. M. Schebesch, R. Zoephel, and A. Brawanski. A mechanism for rapid development of intracranial aneurysms: a case study. Neurosurgery 67:1213–1221, 2010.PubMedCrossRefGoogle Scholar
  12. 12.
    Dolan, J. M., H. Meng, S. Singh, R. Paluch, and J. Kolega. High fluid shear stress and spatial shear stress gradients affect endothelial proliferation, survival, and alignment. Ann. Biomed. Eng. 39:1620–1631, 2011.PubMedCrossRefGoogle Scholar
  13. 13.
    Ford, M. D., Y. Hoi, M. Piccinelli, L. Antiga, and D. A. Steinman. An objective approach to digital removal of saccular aneurysms: technique and applications. Br. J. Radiol. 82:55–61, 2009.CrossRefGoogle Scholar
  14. 14.
    Glor, F. P., B. Ariff, A. D. Hughes, L. A. Crowe, P. R. Verdonck, D. C. Barratt, S. A. McG Thom, D. N. Firmin, and X. Y. Xu. Image-based carotid flow reconstruction: a comparison between MRI and ultrasound. Physiol. Meas. 25:1495–1509, 2004.Google Scholar
  15. 15.
    Glor, F. P., Q. Long, A. D. Hughes, A. D. Augst, B. Ariff, S. A. Thom, P. R. Verdonck, and X. Y. Xu. Reproducibility study of magnetic resonance image-based computational fluid dynamics prediction of carotid bifurcation flow. Ann. Biomed. Eng. 31:142–151, 2002.CrossRefGoogle Scholar
  16. 16.
    He, X., and D. N. Ku. Pulsatile flow in the human left coronary artery bifurcation: average conditions. J. Biomech. Eng. 118(1):74–82, 1996.PubMedCrossRefGoogle Scholar
  17. 17.
    Hoi, Y., H. Meng, S. H. Woodward, B. R. Bendok, R. A. Hanel, L. R. Guterman, and L. N. Hopkins. Effects of arterial geometry on aneurysm growth: three-dimensional computational fluid dynamics study. J. Neurosurg. 101(4):676–81, 2004.PubMedCrossRefGoogle Scholar
  18. 18.
    Humphrey, J. D., and C. A. Taylor. Intracranial and abdominal aortic aneurysms: similarities, differences, and need for a new class of computational models. Annu. Rev. Biomed. Eng. 10:221–246, 2008.PubMedCrossRefGoogle Scholar
  19. 19.
    Jou, L. D., and M. E. Mawad. Hemodynamic effect of neuroform stent on intimal hyperplasia and thrombus formation in a carotid aneurysm. Med. Biol. Eng. Comput. 33:573–580, 2011.Google Scholar
  20. 20.
    Jou, L. D., and M. E. Mawad. Timing and size of flow impingement in a giant intracranial aneurysm at the internal carotid artery. Med. Biol. Eng. Comput. 49:891–899, 2011.PubMedCrossRefGoogle Scholar
  21. 21.
    Juvela, S. Treatment options of unruptured intracranial aneurysms. Stroke 35:372–374, 2004.PubMedCrossRefGoogle Scholar
  22. 22.
    Kanematsu, Y., M. Kanematsu, C. Kurihara, Y. Tada, T.-L. Tsou, N. van Rooijen, M. T. Lawton, W. L. Young, E. I. Liang, Y. Nuki, and T. Hashimoto. Critical roles of macrophages in the formation of intracranial aneurysm. Stroke 42(1):173–178, 2011.PubMedCrossRefGoogle Scholar
  23. 23.
    Karmonik, C., A. Arat, G. Benndorf, S. Akpek, R. Klucznik, M. E. Mawad, and C. M. Strother. A technique for improved quantitative characterization of intracranial aneurysms. Am. J. Neuroradiol. 25:1158–1161, 2004.PubMedGoogle Scholar
  24. 24.
    Ku, D. N., D. P. Giddens, C. K. Zarins, and S. Glagov. Pulsatile flow and atherosclerosis in the human carotid bifurcation. positive correlation between plaque location and low oscillating shear stress. Arterioscler. Thrombosis Vasc. Biol. 5(3):293–302, 1985.CrossRefGoogle Scholar
  25. 25.
    Kulcsár, Z., A. Ugron, M. Marosfoi, Z. Berentei, G. Paál, and I. Szikora. Hemodynamics of cerebral aneurysm initiation: the role of wall shear stress and spatial wall shear stress gradient. Am. J. Neuroradiol. 32:587–594, 2011.PubMedCrossRefGoogle Scholar
  26. 26.
    Mantha, A., C. Karmonik, G. Benndorf, C. Strother, and R. Metcalfe. Hemodynamics in a cerebral artery before and after the formation of an aneurysm. Am. J. Neuroradiol. 27:1113–1118, 2006.PubMedGoogle Scholar
  27. 27.
    Meng, H., E. Metaxa, L. Gao, N. Liaw, S. K. Natarajan, D. D. Swartz, A. H. Siddiqui, J. Kolega, and J. Mocco. Progressive aneurysm development following hemodynamic insult. J. Neurosurg. 114(4):1095–1103, 2011.PubMedCrossRefGoogle Scholar
  28. 28.
    Meng, H., D. D. Swart, Z. Wang, Y. Hoi, J. Kolega, E. Metaxa, and M. P. Szymanski. A model system for mapping vascular responses to complex hemodynamics at arterial bifurcations in vivo. Neurosurgery 59(5):1094–1100, 2006.PubMedGoogle Scholar
  29. 29.
    Meng, H., Z. Wang, Y. Hoi, L. Gao, E. Metaxa, D. D. Swartz, J. Kolega, and D. D. Swart. Complex hemodynamics at the apex of an arterial bifurcation induces vascular remodelling resembling cerebral aneurysm initiation. Stroke 38(6):1924–1931, 2007.PubMedCrossRefGoogle Scholar
  30. 30.
    Metaxa, E., M. Tremmel, S. K. Natarajan, J. Xiang, R. A. Paluch, M. Mandelbaum, and A. H. Siddiqui. Characterization of critical hemodynamics contributing to aneurysmal remodeling at the basilar terminus in a rabbit model. Stroke 41(8):1774–1782, 2010.PubMedCrossRefGoogle Scholar
  31. 31.
    Patankar, S. V. Numerical Heat Transfer and Fluid Flow. Hemisphere Series on Computational Methods in Mechanics and Thermal Science. Washington/New York: Hemisphere Pub. Corp./McGraw-Hill, 1980.Google Scholar
  32. 32.
    Regan, E. R., and W. C. Aird. Dynamical systems approach to endothelial heterogeneity. Circ. Res. 111(1):110–130, 2012.PubMedCrossRefGoogle Scholar
  33. 33.
    Reymond, P., Y. Bohraus, F. Perren, F. Lazeyras, and N. Stergiopulos. Validation of a patient-specific one-dimensional model of the systemic arterial tree. AJP 301:1173–1182, 2011.Google Scholar
  34. 34.
    Reymond, P., F. Merenda, F. Perren, D. Rufenacht, and N. Stergiopulos. Validation of a one-dimensional model of the systemic arterial tree. AJP 297:208–222, 2009.Google Scholar
  35. 35.
    Rinkel, G. J., M. Djibuti, A. Algra, and J. V. Gijn. Prevalence and risk of rupture of intracranial aneurysms: a systematic review. Stroke 29:251–256, 1998.PubMedCrossRefGoogle Scholar
  36. 36.
    Robertson, A. M., and P. N. Watton. Computational fluid dynamics in aneurysm research: critical reflections, future directions. Am. J. Neuroradiol. 33(6):992–995, 2012.PubMedCrossRefGoogle Scholar
  37. 37.
    Shimogonya, Y., T. Ishikawa, Y. Imai, N. Matsuki, and T. Yamaguchi. A realistic simulation of saccular cerebral aneurysm formation: focussing on a novel haemodynamic index, the gradient oscillatory number. Int. J. Comput. Fluid Dyn. 23:583–593, 2009.CrossRefGoogle Scholar
  38. 38.
    Shimogonya, Y., T. Ishikawa, Y. Imai, N. Matsuki, and T. Yamaguchi. Can temporal fluctuation in spatial wall shear stress gradient initiate a cerebral aneurysm? A proposed novel hemodynamic index, the gradient oscillatory number (GON). J. Biomech. 42:550–554, 2009.PubMedCrossRefGoogle Scholar
  39. 39.
    Shimogonya, Y., H. Kumamaru, and K. Itoh. Sensitivity of the gradient oscillatory number to flow input waveform shapes. J. Biomech. 45:985–989, 2012.PubMedCrossRefGoogle Scholar
  40. 40.
    Singh, P. K., A. Marzo, B. Howard, D. A. Rufenacht, P. Bijlenga, A. F. Frangi, P. V. Lawford, S. C. Coley, D. R. Hose, and U. J. Patel. Effects of smoking and hypertension on wall shear stress and oscillatory shear index at the site of intracranial aneurysm formation. Clin. Neurol. Neurosurg. 112(4):306–313, 2010.PubMedCrossRefGoogle Scholar
  41. 41.
    Tremmel, M., J. Xiang, Y. Hoi, J. Kolega, A. H. Siddiqui, J. Mocco, and H. Meng. Mapping vascular response to in vivo hemodynamics: application to increased flow at the basilar terminus. Biomech. Model. Mechanobiol. 9:421–434, 2010.PubMedCrossRefGoogle Scholar
  42. 42.
    Villa-Uriol, M. C., G. Berti, D. R. Hose, A. Marzo, A. Chiarini, J. Penrose, J. Pozo, J. G. Schmidt, P. Singh, R. Lycett, I. Larrabide, and A. F. Frangi. @neurist complex information processing toolchain for the integrated management of cerebral aneurysms. Interface Focus 1:308–319, 2011.PubMedCrossRefGoogle Scholar
  43. 43.
    Vlak, M. H., A. Algra, R. Brandenburg, and G. J. Rinkel. Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. Lancet Neurol. 10(7):626–636, 2011.PubMedCrossRefGoogle Scholar
  44. 44.
    Watton, P. N., H. Huang, and Y. Ventikos. Multi-scale modelling of vascular disease: abdominal aortic aneurysm evolution. In: Computational Modeling in Tissue Engineering, volume 10 of Studies in Mechanobiology, Tissue Engineering and Biomaterials, edited by L. Geris. Springer, Berlin, 2013, pp. 309–339.Google Scholar
  45. 45.
    Watton, P. N., Y. Ventikos, and G. A. Holzapfel. Modelling cerebral aneurysm evolution. In: Biomechanics and Mechanobiology of Aneurysms, Vol. 7 of Studies in Mechanobiology, Tissue Engineering and Biomaterials, chapter 12, edited by T. McGloughlin. Heidelberg: Springer, pp. 307-322, 2011.Google Scholar
  46. 46.
    Wolfe, S. Q., M. K. Bakaya, R. C. Heros, R. P. Tummala. Cerebral aneurysms: learning fom the past and looking toward the future. Clin. Neurosurg. 53:157–178, 2006.PubMedGoogle Scholar
  47. 47.
    Zeng, Z., D. F. Kallmes, M. J. Durka, Y. Ding, D. A. Lewis, R. Kadirvel, and A. M. Robertson. Hemodynamics and anatomy of elastase-induced rabbit aneurysm modelssimilarity to human cerebral aneurysms? Am. J. Neuroradiol. 32:595–601, 2011.PubMedCrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2013

Authors and Affiliations

  • Haoyu Chen
    • 1
  • Alisa Selimovic
    • 1
  • Harry Thompson
    • 1
  • Alessandro Chiarini
    • 2
  • Justin Penrose
    • 3
  • Yiannis Ventikos
    • 1
  • Paul N. Watton
    • 1
    Email author
  1. 1.Institute of Biomedical Engineering Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.BioComputing Competence Centre, B3CSCS srlCasalecchio di RenoItaly
  3. 3.ANSYS-UK Ltd.AbingdonUK

Personalised recommendations