Skip to main content
SpringerLink
Log in

CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamics

  • Conference paper
  • First Online:
Cerebral Aneurysm Detection and Analysis (CADA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12643))

Included in the following conference series:

  • 583 Accesses

Abstract

The phase 3 of the cerebral aneurysm detection and analysis (CADA) challenge involved rupture risk estimation of intracranial aneurysms using computational methods. In this work we performed computational fluid dynamics (CFD) on a subset of aneurysm cases provided by the challenge committee. A large number of aneurysm cases were available, CFD analysis using the lattice Boltzmann method (LBM) were performed on 18 of them. These 18 aneurysms were chosen on the basis of most distinct shape, size and location. Direct numerical simulations were performed to identify wall shear stress and pressure, and associate these hemodynamic quantities with the rupture status of aneurysms and eventually extrapolate those findings to other aneurysms. The results of the DNS may serve as inputs for data driven methods to identify qualitative maps of hemodynamic quantities in aneurysms. In this article we report the results of CFD and discuss hypotheses associating the flow characteristics with the rupture risk of aneurysms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://cada.grand-challenge.org.

  2. 2.

    www.surfsara.nl.

References

  1. Chung, B., Cebral, J.R.: CFD for evaluation and treatment planning of aneurysms: review of proposed clinical uses and their challenges. Ann. Biomed. Eng. 43(1), 1–17 (2014)

    Google Scholar 

  2. Xiang, J., et al.: Hemodynamic-morphologic discriminants for intracranial aneurysm rupture. Stroke 42(1), 144–152 (2011)

    Article  Google Scholar 

  3. Lu, G., et al.: Influence of hemodynamic factors on rupture of intracranial aneurysms: patient-specific 3D mirror aneurysms model computational fluid dynamics simulation. Am. J. Neuroradiol. 32(7), 1255–1261 (2011)

    Article  Google Scholar 

  4. Chien, A., Tateshima, S., Castro, M., Sayre, J., Cebral, J., Vinuela, F.: Patient-specific flow analysis of brain aneurysms at a single location: comparison of hemodynamic characteristics in small aneurysms. Med. Biol. Eng. Comput. 46(11), 1113–1120 (2008)

    Article  Google Scholar 

  5. Shojima, M., et al.: Magnitude and role of wall shear stress on cerebral aneurysm: computational fluid dynamic study of 20 middle cerebral artery aneurysms. Stroke 35(11), 2500–2505 (2004)

    Article  Google Scholar 

  6. Hassan, T., et al.: Computational simulation of therapeutic parent artery occlusion to treat giant vertebrobasilar aneurysm. Am. J. Neuroradiol. 25(1), 63–68 (2004)

    MathSciNet  Google Scholar 

  7. Jain, K., Roller, S., Mardal, K.-A.: Transitional flow in intracranial aneurysms-a space and time refinement study below the Kolmogorov scales using lattice Boltzmann method. Comput. Fluids 127, 36–46 (2016). https://doi.org/10.1016/j.compfluid.2015.12.011

    Article  MathSciNet  MATH  Google Scholar 

  8. Valen-Sendstad, K., Steinman, D.A.: Mind the gap: impact of computational fluid dynamics solution strategy on prediction of intracranial aneurysm hemodynamics and rupture status indicators. Am. J. Neuroradiol. 35(3), 536–543 (2013)

    Article  Google Scholar 

  9. Dennis, K.D., Kallmes, D.F., Dragomir-Daescu, D.: Further discussion of "cerebral aneurysm blood flow simulations are sensitive to basic solver settings". J. Biomech. 61, 281–282 (2017)

    Article  Google Scholar 

  10. Radaelli, A.G., et al.: Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model-a report on the virtual intracranial stenting challenge 2007. J. Biomech. 41(10), 2069–2081 (2008)

    Article  Google Scholar 

  11. Steinman, D.A., et al.: Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 summer bioengineering conference CFD challenge. J. Biomech. Eng. 135(2), 021016 (2013)

    Article  Google Scholar 

  12. Janiga, G., Berg, P., Sugiyama, S., Kono, K., Steinman, D.A.: The computational fluid dynamics rupture challenge 2013–Phase I: prediction of rupture status in intracranial aneurysms. Am. J. Neuroradiol. 36(3), 530–536 (2015)

    Article  Google Scholar 

  13. Berg, P., et al.: The computational fluid dynamics rupture challenge 2013-phase II: variability of hemodynamic simulations in two intracranial aneurysms. J. Biomech. Eng. 137(12), 121008 (2015)

    Article  Google Scholar 

  14. Valen-Sendstad, K., et al.: Real-world variability in the prediction of intracranial aneurysm wall shear stress: the 2015 international aneurysm CFD challenge. Cardiovasc. Eng. Technol. 9(4), 544–564 (2018). https://doi.org/10.1007/s13239-018-00374-2

    Article  Google Scholar 

  15. Kossen, C.T., et al.: Cerebral aneurysm detection and analysis. (2020). https://doi.org/10.5281/zenodo.3715012

  16. Roller, S., et al.: An adaptable simulation framework based on a linearized octree. In: Resch, M., Wang, X., Bez, W., Focht, E., Kobayashi, H., Roller, S. (eds.) High Performance Computing on Vector Systems 2011. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22244-3_7

  17. Klimach, H., Jain, K., Roller, S.: End-to-end parallel simulations with apes. Parallel Comput. Accelerating Comput. Sci. Eng. (CSE) 25, 703–711 (2014)

    Google Scholar 

  18. Harlacher, D.F., Hasert, M., Klimach, H., Zimny, S., Roller, S.: Tree based voxelization of STL data. In: Resch, M., Wang,, X., Bez, W., Focht, E., Kobayashi, H., Roller, S. (eds.) High Performance Computing on Vector Systems 2011. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22244-3_6

  19. Junk, M., Yang, Z.: Asymptotic analysis of lattice Boltzmann outflow treatments. Commun. Comput. Phys. 9(5), 1117–1127 (2011)

    Article  Google Scholar 

  20. Bouzidi, M.H., Firdaouss, M., Lallemand, P.: Momentum transfer of a Boltzmann-lattice fluid with boundaries. Phys. Fluids 13, 3452 (2001)

    Article  Google Scholar 

  21. Krejza, J., et al.: Age and sex variability and normal reference values for the vmca/vica index. Am. J. Neuroradiol. 26(4), 730–735 (2005)

    Google Scholar 

  22. Jain, K., Jiang, J., Strother, C., Mardal, K.-A.: Transitional hemodynamics in intracranial aneurysms - comparative velocity investigations with high resolution lattice Boltzmann simulations, normal resolution ANSYS simulations and MR imaging. Med. Phys. 43, 6186–6198 (2016). https://doi.org/10.1118/1.4964793. PMID:27806613

    Article  Google Scholar 

  23. Jain, K.: Efficacy of the FDA nozzle benchmark and the lattice Boltzmann method for the analysis of biomedical flows in transitional regime. Med. Biol. Eng. Comput. 58, 1817–1830 (2020). https://doi.org/10.1007/s11517-020-02188-8. PMID:32507933

    Article  Google Scholar 

  24. Hasert, M., et al.: Complex fluid simulations with the parallel tree-based lattice Boltzmann solver Musubi. J. Comput. Sci. 5(5), 784–794 (2014)

    Article  MathSciNet  Google Scholar 

  25. Strother, C.M., Jiang, J.: Intracranial aneurysms, cancer, X-rays, and computational fluid dynamics. Am. J. Neuroradiol. 33(6), 991–992 (2012)

    Article  Google Scholar 

  26. Raghavan, M.L., Ma, B., Harbaugh, R.E.: Quantified aneurysm shape and rupture risk. J. Neurosurg. 102(2), 355–362 (2005)

    Article  Google Scholar 

  27. Juchler, N., et al.: Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms. Comput. Method Biomech. Biomed. Eng. Imaging Visual. 8(5), 538–546 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500, Enschede, AE, The Netherlands

    Kartik Jain

Authors
  1. Kartik Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kartik Jain .

Editor information

Editors and Affiliations

  1. Charité – Universitätsmedizin Berlin, Berlin, Germany

    Anja Hennemuth

  2. Charité – Universitätsmedizin Berlin, Berlin, Germany

    Leonid Goubergrits

  3. Charité – Universitätsmedizin Berlin, Berlin, Germany

    Matthias Ivantsits

  4. Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany

    Jan-Martin Kuhnigk

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jain, K. (2021). CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamics. In: Hennemuth, A., Goubergrits, L., Ivantsits, M., Kuhnigk, JM. (eds) Cerebral Aneurysm Detection and Analysis. CADA 2020. Lecture Notes in Computer Science(), vol 12643. Springer, Cham. https://doi.org/10.1007/978-3-030-72862-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72862-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72861-8

  • Online ISBN: 978-3-030-72862-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation