Skip to main content

Advertisement

SpringerLink
Log in

Challenges in Modeling Hemodynamics in Cerebral Aneurysms Related to Arteriovenous Malformations

  • Original Article
  • Published:
Cardiovascular Engineering and Technology Aims and scope Submit manuscript

Abstract

Purpose

The significantly higher incidence of aneurysms in patients with arteriovenous malformations (AVMs) suggests a strong hemodynamic relationship between these lesions. The presence of an AVM alters hemodynamics in proximal vessels by drastically changing the distal resistance, thus affecting intra-aneurysmal flow. This study discusses the challenges associated with patient-specific modeling of aneurysms in the presence of AVMs.

Methods

We explore how the presence of a generic distal AVM affects upstream aneurysms by examining the relationship between distal resistance and aneurysmal wall shear stress using physiologically realistic estimates for the influence of the AVM on hemodynamics. Using image-based computational models of aneurysms and surrounding vasculature, aneurysmal wall-shear stress is calculated for a range of distal resistances corresponding to the presence of AVMs of various sizes and compared with a control case representing the absence of an AVM.

Results

In the patient cases considered, the alteration in aneurysmal wall shear stress due to the presence of an AVM is considerable, as much as 19 times the base case wall shear stress. Furthermore, the relationship between aneurysmal wall shear stress and distal resistance is shown to be highly geometry-dependent and nonlinear. In most cases, the range of physiologically realistic possibilities for AVM-related distal resistance are so large that patient-specific flow measurements are necessary for meaningful predictions of wall shear stress.

Conclusions

The presented work offers insight on the impact of distal AVMs on aneurysmal wall shear stress using physiologically realistic computational models. Patient-specific modeling of hemodynamics in aneurysms and associated AVMs has great potential for understanding lesion pathogenesis, surgical planning, and assessing the effect of treatment of one lesion relative to another. However, we show that modeling approaches cannot usually meaningfully quantify the impact of AVMs if based solely on imaging data from CT and X-ray angiography, currently used in clinical practice. Based on recent studies, it appears that 4D flow MRI is one promising approach to obtaining meaningful patient-specific flow boundary conditions that improve modeling fidelity.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  1. Ansari, S., S. Schnell, T. Carroll, P. Vakil, M. Hurley, C. Wu, J. Carr, B. Bendok, H. Batjer, and M. Markl. Intracranial 4D flow MRI: toward individualized assessment of arteriovenous malformation hemodynamics and treatment-induced changes. Am. J. Neuroradiol. 34(10):1922, 2013. https://doi.org/10.3174/ajnr.A3537.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Aristova, M., A. Vali, S. A. Ansari, A. Shaibani, T. D. Alden, M. C. Hurley, B. S. Jahromi, M. B. Potts, M. Markl, and S. Schnell. Standardized evaluation of cerebral arteriovenous malformations using flow distribution network graphs and dual-venc 4D flow MRI. J. Magn. Reson. Imaging. 50(6):1718, 2019. https://doi.org/10.1002/jmri.26784.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Berg, P., S. Voß, S. Saalfeld, G. Janiga, A. W. Bergersen, K. Valen-Sendstad, J. Bruening, L. Goubergrits, A. Spuler, N. M. Cancelliere, D. A. Steinman, V. M. Pereira, T. L. Chiu, A. C. O. Tsang, B. J. Chung, J. R. Cebral, S. Cito, J. Pallarès, G. Copelli, B. Csippa, G. Paál, S. Fujimura, H. Takao, S. Hodis, G. Hille, C. Karmonik, S. Elias, K. Kellermann, M. O. Khan, A. L. Marsden, H. G. Morales, S. Piskin, E. A. Finol, M. Pravdivtseva, H. Rajabzadeh-Oghaz, N. Paliwal, H. Meng, S. Seshadhri, M. Howard, M. Shojima, S. I. Sugiyama, K. Niizuma, S. Sindeev, S. Frolov, T. Wagner, A. Brawanski, Y. Qian, Y. A. Wu, K. D. Carlson, D. Dragomir-Daescu, and O. Beuing. Multiple aneurysms anatomy challenge 2018 (MATCH): phase I: segmentation. Cardiovasc. Eng. Technol. 9(4):565, 2018. https://doi.org/10.1007/s13239-018-00376-0.

    Article  PubMed  Google Scholar 

  4. Boussel, L., V. Rayz, A. Martin, G. Acevedo-Bolton, M. T. Lawton, R. Higashida, W. S. Smith, W. L. Young, and D. Saloner. Phase-Contrast MRI measurements in intra-cranial aneurysms in-vivo of flow patterns, velocity fields and wall shear stress: a comparison with CFD. Magn. Resonan. Med. 61(2):409, 2009. https://doi.org/10.1002/mrm.21861.

    Article  Google Scholar 

  5. Boussel, L., V. Rayz, C. McCulloch, A. Martin, G. Acevedo-Bolton, M. Lawton, R. Higashida, W. S. Smith, W. L. Young, and D. Saloner. Aneurysm growth occurs at region of low wall shear stress: patient-specific correlation of hemodynamics and growth in a longitudinal study. Stroke. 39(11):2997, 2008. https://doi.org/10.1161/STROKEAHA.108.521617.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Brown, R. D., D. O. Wiebers, and G. S. Forbes. Unruptured intracranial aneurysms and arteriovenous malformations: frequency of intracranial hemorrhage and relationship of lesions. J. Neurosurg. 73(6):859, 1990. https://doi.org/10.3171/jns.1990.73.6.0859.

    Article  PubMed  Google Scholar 

  7. Cebral, J., E. Ollikainen, B. Chung, F. Mut, V. Sippola, B. Jahromi, R. Tulamo, J. Hernesniemi, M. Niemelä, A. Robertson, and J. Frösen. Flow conditions in the intracranial aneurysm lumen are associated with inflammation and degenerative changes of the aneurysm wall. Am. J. Neuroradiol. 38(1):119, 2017. https://doi.org/10.3174/ajnr.A4951.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chang, W., M. W. Loecher, Y. Wu, D. B. Niemann, B. Ciske, B. Aagaard-Kienitz, S. Kecskemeti, K. M. Johnson, O. Wieben, C. Mistretta, and P. Turski. Hemodynamic changes in patients with arteriovenous malformations assessed using high-resolution 3D radial phase-contrast MR angiography. Am. J. Neuroradiol. 33(8):1565, 2012. https://doi.org/10.3174/ajnr.A3010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Detmer, F. J., B. J. Chung, F. Mut, M. Slawski, F. Hamzei-Sichani, C. Putman, C. Jiménez, and J. R. Cebral. Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics. Int. J. Comput. Assist. Radiol. Surg. 13(11):1767, 2008. https://doi.org/10.1007/s11548-018-1837-0.

    Article  Google Scholar 

  10. Duckwiler, G., J. Dion, F. Vinuela, B. Jabour, N. Martin, and J. Bentson. Intravascular microcatheter pressure monitoring: experimental results and early clinical evaluation. Am. J. Neuroradiol. 11(1):169, 1990.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Esmaily Moghadam, M., Y. Bazilevs, T. Y. Hsia, I. E. Vignon-Clementel, and A. L. Marsden. A comparison of outlet boundary treatments for prevention of backflow divergence with relevance to blood flow simulations. Comput. Mech. 48(3):277, 2011. https://doi.org/10.1007/s00466-011-0599-0.

    Article  Google Scholar 

  12. Esmaily Moghadam, M., I. E. Vignon-Clementel, R. Figliola, and A. L. Marsden. A modular numerical method for implicit 0D/3D coupling in cardiovascular finite element simulations. J. Comput. Phys. 244:63, 2013. https://doi.org/10.1016/j.jcp.2012.07.035.

    Article  Google Scholar 

  13. Fogarty-Mack, P., J. Pile-Spellman, L. Hacein-Bey, A. Osipov, J. DeMeritt, E. C. Jackson, and W. L. Young. The effect of arteriovenous malformations on the distribution of intracerebral arterial pressures. Am. J. Neuroradiol. 17(8):1443, 1996.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Francis, C., L. Frederic, L. Sylvie, P. Prasanna, and D. Henri. Scaling laws for branching vessels of human cerebral cortex. Microcirculation. 16(4):331, 2009. https://doi.org/10.1080/10739680802662607.

    Article  Google Scholar 

  15. Frösen, J., J. Cebral, A. M. Robertson, and T. Aoki. Flow-induced, inflammation-mediated arterial wall remodeling in the formation and progression of intracranial aneurysms. Neurosurg. Focus. 47(1):E21, 2019. https://doi.org/10.3171/2019.5.FOCUS19234.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gao, E., W. L. Young, E. Ornstein, J. Pile-Spellman, and M. Qiyuan. A theoretical model of cerebral hemodynamics: application to the study of arteriovenous malformations. J. Cereb. Blood Flow Metab. 17(8):905, 1997. https://doi.org/10.1097/00004647-199708000-00009.

    Article  CAS  PubMed  Google Scholar 

  17. Gao, E., W. L. Young, J. Pile-Spellman, S. Joshi, H. Duong, P. E. Stieg, and Q. Ma. Cerebral arteriovenous malformation feeding artery aneurysms: a theoretical model of intravascular pressure changes after treatment. Neurosurgery. 41(6):1345, 1997. https://doi.org/10.1097/00006123-199712000-00020.

    Article  CAS  PubMed  Google Scholar 

  18. Jungreis, C. A., and J. A. Horton. Pressure changes in the arterial feeder to a cerebral AVM as a guide to monitoring therapeutic embolization. Am. J. Neuroradiol. 10(5):1057, 1989.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Jungreis, C. A., J. A. Horton, and S. T. Hecht. Blood pressure changes in feeders to cerebral arteriovenous malformations during therapeutic embolization. Am. J. Neuroradiol. 10(3):575, 1989.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lan, H., A. Updegrove, N. M. Wilson, G. D. Maher, S. C. Shadden, and A. L. Marsden. A re-engineered software interface and workflow for the open-source simvascular cardiovascular modeling package. J. Biomech. Eng.140(2):245011, 2018. https://doi.org/10.1115/1.4038751.

    Article  Google Scholar 

  21. Levitt, M., P. McGah, A. Aliseda, P. Mourad, J. Nerva, S. Vaidya, R. Morton, B. Ghodke, and L. Kim. Cerebral aneurysms treated with flow-diverting stents: computational models with intravascular blood flow measurements. Am. J. Neuroradiol. 35(1):143, 2014. https://doi.org/10.3174/ajnr.A3624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. MacDonald, M. E., and R. Frayne. Phase contrast MR imaging measurements of blood flow in healthy human cerebral vessel segments. Physiol. Meas. 36(7):1517, 2015. https://doi.org/10.1088/0967-3334/36/7/1517.

    Article  PubMed  Google Scholar 

  23. Markl, M., A. Frydrychowicz, S. Kozerke, M. Hope, and O. Wieben. 4D flow MRI. J. Magn. Reson. Imaging. 36(5):1015, 2012. https://doi.org/10.1002/jmri.23632.

    Article  PubMed  Google Scholar 

  24. Marsden, A. L., and M. Esmaily-Moghadam. Multiscale modeling of cardiovascular flows for clinical decision support. Appl. Mech. Rev. 67(3):030804, 2015. https://doi.org/10.1115/1.4029909.

    Article  Google Scholar 

  25. McElroy, M., and A. Keshmiri. Impact of using conventional inlet/outlet boundary conditions on haemodynamic metrics in a subject-specific rabbit aorta. Proc. Inst. Mech. Eng. Part H: J. Eng. Med. 232(2):103, 2018. https://doi.org/10.1177/0954411917699237.

    Article  Google Scholar 

  26. Meng, H., V. M. Tutino, J. Xiang, and A. Siddiqui. High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis. Am. J. Neuroradiol. 35(7):1254, 2014. https://doi.org/10.3174/ajnr.A3558.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Nornes, H., and A. Grip. Steal and cerebral arteriovenous malformations. J. Neurosurg. 53(4):456, 1980. https://doi.org/10.3171/jns.1980.53.4.0456.

    Article  CAS  PubMed  Google Scholar 

  28. Rammos, S. K., B. Gardenghi, C. Bortolotti, H. J. Cloft, and G. Lanzino. Aneurysms associated with brain arteriovenous malformations. Am. J. Neuroradiol. 37(11):1966, 2016. https://doi.org/10.3174/ajnr.A4869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rayz, V. L., A. Abla, L. Boussel, J. R. Leach, G. Acevedo-Bolton, D. Saloner, and M. T. Lawton. Computational modeling of flow-altering surgeries in basilar aneurysms. Ann. Biomed. Eng. 43(5):1210, 2015. https://doi.org/10.1007/s10439-014-1170-x.

    Article  CAS  PubMed  Google Scholar 

  30. Rayz, V. L., L. Boussel, L. Ge, J. R. Leach, A. J. Martin, M. T. Lawton, C. McCulloch, and D. Saloner. Flow residence time and regions of intraluminal thrombus deposition in intracranial aneurysms. Ann. Biomed. Eng. 38(10):3058, 2010. https://doi.org/10.1007/s10439-010-0065-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rayz, V., L. Boussel, M. Lawton, G. Acevedo-Bolton, L. Ge, W. Young, R. Higashida, and D. Saloner. Numerical modeling of the flow in intracranial aneurysms: prediction of regions prone to thrombus formation. Ann. Biomed. Eng. 36(11):1793, 2008. https://doi.org/10.1007/s10439-008-9561-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rayz, V. L., M. T. Lawton, A. J. Martin, W. L. Young, and D. Saloner. Numerical simulation of pre-and postsurgical flow in a giant basilar aneurysm. J. Biomech. Eng. 130(2):021004, 2008. https://doi.org/10.1115/1.2898833.

    Article  PubMed  Google Scholar 

  33. Redekop, G., K. TerBrugge, W. Montanera, and R. Willinsky. Arterial aneurysms associated with cerebral arteriovenous malformations: classification, incidence, and risk of hemorrhage. J. Neurosurg. 89(4):539, 1998. https://doi.org/10.3171/jns.1998.89.4.0539.

    Article  CAS  PubMed  Google Scholar 

  34. Rossitti, S., and J. Löfgren. Vascular dimensions of the cerebral arteries follow the principle of minimum work. Stroke. 24(3):371, 1993. https://doi.org/10.1161/01.STR.24.3.371.

    Article  CAS  PubMed  Google Scholar 

  35. Seo, J., D. E. Schiavazzi, A. M. Kahn, and A. L. Marsden. The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls. Int. J. Numer. Methods Biomed. Eng.36(8):e3351, 2020. https://doi.org/10.1002/cnm.3351.

    Article  Google Scholar 

  36. Shakur, S. F., S. Amin-Hanjani, M. Abouelleil, V. A. Aletich, F. T. Charbel, and A. Alaraj. Changes in pulsatility and resistance indices of cerebral arteriovenous malformation feeder arteries after embolization and surgery. Neurol. Res. 39(1):7, 2017. https://doi.org/10.1080/01616412.2016.1258970.

    Article  PubMed  Google Scholar 

  37. Shakur, S. F., S. Amin-Hanjani, H. Mostafa, F. T. Charbel, and A. Alaraj. Hemodynamic characteristics of cerebral arteriovenous malformation feeder vessels with and without aneurysms. Stroke. 46(7):1997, 2015. https://doi.org/10.1161/STROKEAHA.115.009545.

    Article  PubMed  Google Scholar 

  38. Spetzler, R. F., R. W. Hargraves, P. W. McCormick, J. M. Zabramski, R. A. Flom, and R. S. Zimmerman. Relationship of perfusion pressure and size to risk of hemorrhage from arteriovenous malformations. J. Neurosurg. 76(6):918, 1992. https://doi.org/10.3171/jns.1992.76.6.0918.

    Article  CAS  PubMed  Google Scholar 

  39. Sturdy, J., J. K. Kjernlie, H. M. Nydal, V. G. Eck, and L. R. Hellevik. Uncertainty quantification of computational coronary stenosis assessment and model based mitigation of image resolution limitations. J. Comput. Sci. 31:137, 2019. https://doi.org/10.1016/j.jocs.2019.01.004.

    Article  Google Scholar 

  40. Tanaka, M. In Brain Arteriovenous Malformations: Pathogenesis, Epidemiology, Diagnosis, Treatment and Outcome, edited by V. Benes and O. Bradac. Cham: Springer International Publishing, 2017, pp. 5–22. https://doi.org/10.1007/978-3-319-63964-2_2.

  41. Taylor, C. A., T. A. Fonte, and J. K. Min. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J. Am. Coll. Cardiol. 61(22):2233, 2013. https://doi.org/10.1016/j.jacc.2012.11.083.

    Article  PubMed  Google Scholar 

  42. Updegrove, A., N. M. Wilson, J. Merkow, H. Lan, A. L. Marsden, and S. C. Shadden. SimVascular: an open source pipeline for cardiovascular simulation. Ann. Biomed. Eng. 45(3):525, 2017. https://doi.org/10.1007/s10439-016-1762-8.

    Article  PubMed  Google Scholar 

  43. Vali, A., A. A. Abla, M. T. Lawton, D. Saloner, and V. L. Rayz. Computational fluid dynamics modeling of contrast transport in basilar aneurysms following flow-altering surgeries. J. Biomech. 50:195, 2017. https://doi.org/10.1016/j.jbiomech.2016.11.028.

    Article  PubMed  Google Scholar 

  44. van der Giessen, A. G., H. C. Groen, P. A. Doriot, P. J. de Feyter, A. F. van der Steen, F. N. van de Vosse, J. J. Wentzel, and F. J. Gijsen. The influence of boundary conditions on wall shear stress distribution in patients specific coronary trees. J. Biomech. 44(6):1089, 2011. https://doi.org/10.1016/j.jbiomech.2011.01.036.

    Article  PubMed  Google Scholar 

  45. Whiting, C. H., and K. E. Jansen. A stabilized finite element method for the incompressible Navier-Stokes equations using a hierarchical basis. Int. J. Numer. Methods Fluids. 35(1):93, 2001.

    Article  CAS  Google Scholar 

  46. Yushkevich, P. A., J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig. User-guided level set segmentation of anatomical structures with ITK-SNAP. NeuroImage. 31(3):1116, 2006. https://doi.org/10.1016/j.neuroimage.2006.01.015.

    Article  PubMed  Google Scholar 

  47. Zarrinkoob, L., K. Ambarki, A. Wåhlin, R. Birgander, A. Eklund, and J. Malm. Blood flow distribution in cerebral arteries. J. Cereb. Blood Flow Metab. 35(4):648, 2015. https://doi.org/10.1038/jcbfm.2014.241.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Zhang, J., S. Bhattacharya, and P. P. Vlachos. Using uncertainty to improve pressure field reconstruction from PIV/PTV flow measurements. Exp. Fluids. 61(6):131, 2020. https://doi.org/10.1007/s00348-020-02974-y.

    Article  CAS  Google Scholar 

  49. Zhao, M., S. Amin-Hanjani, S. Ruland, A. Curcio, L. Ostergren, and F. Charbel. Regional cerebral blood flow using quantitative MR angiography. Am. J. Neuroradiol. 28(8):1470, 2007. https://doi.org/10.3174/ajnr.A0582.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhou, Y., G. S. Kassab, and S. Molloi. On the design of the coronary arterial tree: a generalization of Murray’s law. Phys. Med. Biol. 44(12):2929, 1999. https://doi.org/10.1088/0031-9155/44/12/306.

    Article  CAS  PubMed  Google Scholar 

  51. Zhou, G., Y. Zhu, Y. Yin, M. Su, and M. Li. Association of wall shear stress with intracranial aneurysm rupture: systematic review and meta-analysis. Sci. Rep. 7(1):5331, 2017. https://doi.org/10.1038/s41598-017-05886-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

K.A.S.B. was supported in part by the Lillian Gilbreth Postdoctoral Fellowship from Purdue’s College of Engineering. K.A.S.B. and I.C.C. additionally received partial support from the US National Science Foundation under grant No. CBET-1705637.

Conflict of interest

Kimberly Boster, Tanmay Shidhore, Aaron Cohen-Gadol, Ivan Christov, and Vitaliy Rayz declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Kimberly A. Stevens Boster, Tanmay C. Shidhore & Ivan C. Christov

  2. Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

    Aaron A. Cohen-Gadol

  3. Weldon School of Biomedical Engineering, School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Vitaliy L. Rayz

Authors
  1. Kimberly A. Stevens Boster
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tanmay C. Shidhore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aaron A. Cohen-Gadol
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan C. Christov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vitaliy L. Rayz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kimberly A. Stevens Boster.

Additional information

Associate Editor Ajit P. Yoganathan oversaw the review of this article.

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 103 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boster, K.A.S., Shidhore, T.C., Cohen-Gadol, A.A. et al. Challenges in Modeling Hemodynamics in Cerebral Aneurysms Related to Arteriovenous Malformations. Cardiovasc Eng Tech 13, 673–684 (2022). https://doi.org/10.1007/s13239-022-00609-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13239-022-00609-3

Keywords

Search

Navigation