Skip to main content
SpringerLink
Log in

Uncertainty Quantification of Coupled 1D Arterial Blood Flow and 3D Tissue Perfusion Models Using the INSIST Framework

  • Conference paper
  • First Online:
Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12747))

Included in the following conference series:

Abstract

We perform uncertainty quantification on a one-dimensional arterial blood flow model and investigate the resulting uncertainty in a coupled tissue perfusion model of the brain. The application of interest for this study is acute ischemic stroke. The outcome of interest is infarct volume, estimated using the change in perfusion between the healthy and occluded state (assuming no treatment). Secondary outcomes are the uncertainty in blood flow at the outlets of the network, which provide the boundary conditions to the pial surface of the brain in the tissue perfusion model. Uncertainty in heart stroke volume, heart rate, blood density, and blood viscosity are considered. Results show uncertainty in blood flow at the network outlets is similar to the uncertainty included in the inputs, however the resulting uncertainty in infarct volume is significantly smaller. These results provide evidence when assessing the credibility of the coupled models for use in in silico clinical trials.

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

References

  1. American Society of Mechanical Engineers: Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices (2018)

    Google Scholar 

  2. Chen, P., Quarteroni, A., Rozza, G.: Simulation-based uncertainty quantification of human arterial network hemodynamics. Int. J. Numer. Meth. Biomed. Eng. 29(6), 698–721 (2013). https://doi.org/10.1002/cnm.2554

    Article  MathSciNet  Google Scholar 

  3. Eck, V.G., Sturdy, J., Hellevik, L.R.: Effects of arterial wall models and measurement uncertainties on cardiovascular model predictions. J. Biomech. 50, 188–194 (2017). https://doi.org/10.1016/j.jbiomech.2016.11.042

    Article  Google Scholar 

  4. Eck, V.G., et al.: A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications. Int. J. Numer. Meth. Biomed. Eng. 32(8), e02755 (2016). https://doi.org/10.1002/cnm.2755

    Article  MathSciNet  Google Scholar 

  5. Jansen, I.G.H., Mulder, M.J.H.L., Goldhoorn, R.J.B.: Endovascular treatment for acute ischaemic stroke in routine clinical practice: prospective, observational cohort study (MR CLEAN Registry). BMJ 360, (2018). https://doi.org/10.1136/bmj.k949

  6. Jansen, M.J.W.: Analysis of variance designs for model output. Comput. Phys. Commun. 117(1), 35–43 (1999). https://doi.org/10.1016/S0010-4655(98)00154-4

    Article  MATH  Google Scholar 

  7. Jeong, S.K., Rosenson, R.S.: Shear rate specific blood viscosity and shear stress of carotid artery duplex ultrasonography in patients with lacunar infarction. BMC Neurol. 13(1), 36 (2013). https://doi.org/10.1186/1471-2377-13-36

    Article  Google Scholar 

  8. Józsa, T.I., Padmos, R.M., Samuels, N., El-Bouri, W.K., Hoekstra, A.G., Payne, S.J.: A porous circulation model of the human brain for in silico clinical trials in ischaemic stroke. Interface Focus 11(1), 20190127 (2021). https://doi.org/10.1098/rsfs.2019.0127

    Article  Google Scholar 

  9. Kenner, T.: The measurement of blood density and its meaning. Basic Res. Cardiol. 84(2), 111–124 (1989). https://doi.org/10.1007/BF01907921

    Article  Google Scholar 

  10. Konduri, P.R., Marquering, H.A., van Bavel, E.E., Hoekstra, A., Majoie, C.B.L.M.: INSIST Investigators: In-silico trials for treatment of acute ischemic stroke. Front. Neurol. 11 (2020). https://doi.org/10.3389/fneur.2020.558125

  11. Maceira, A.M., Prasad, S.K., Khan, M., Pennell, D.J.: Normalized left ventricular systolic and diastolic function by steady state free precession cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 8(3), 417–426 (2006). https://doi.org/10.1080/10976640600572889

    Article  Google Scholar 

  12. Mason, J.W., Ramseth, D.J., Chanter, D.O., Moon, T.E., Goodman, D.B., Mendzelevski, B.: Electrocardiographic reference ranges derived from 79,743 ambulatory subjects. J. Electrocardiol. 40(3), 228–234 (2007). https://doi.org/10.1016/j.jelectrocard.2006.09.003

  13. Najm, H.N.: Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics. Annu. Rev. Fluid Mech. 41(1), 35–52 (2009). https://doi.org/10.1146/annurev.fluid.010908.165248

    Article  MathSciNet  MATH  Google Scholar 

  14. Nikishova, A., Hoekstra, A.G.: Semi-intrusive uncertainty propagation for multiscale models. J. Computat. Sci. 35, 80–90 (2019). https://doi.org/10.1016/j.jocs.2019.06.007

    Article  Google Scholar 

  15. Padmos, R.M., Józsa, T.I., El-Bouri, W.K., Konduri, P.R., Payne, S.J., Hoekstra, A.G.: Coupling one-dimensional arterial blood flow to three-dimensional tissue perfusion models for in silico trials of acute ischaemic stroke. Interface Focus 11(1), 20190125 (2021). https://doi.org/10.1098/rsfs.2019.0125

    Article  Google Scholar 

  16. Richardson, R.A., Wright, D.W., Edeling, W., Jancauskas, V., Lakhlili, J., Coveney, P.V.: EasyVVUQ: a library for verification, validation and uncertainty quantification in high performance computing. J. Open Res. Softw. 8(1) (2020). https://doi.org/10.5334/jors.303

  17. Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S.: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun. 181(2), 259–270 (2010). https://doi.org/10.1016/j.cpc.2009.09.018

  18. Xiu, D., Sherwin, S.J.: Parametric uncertainty analysis of pulse wave propagation in a model of a human arterial network. J. Comput. Phys. 226(2), 1385–1407 (2007). https://doi.org/10.1016/j.jcp.2007.05.020

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

INSIST has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 777072.

Author information

Authors and Affiliations

  1. Computational Science Laboratory, Faculty of Science, Informatics Institute, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands

    Claire Miller, Max van der Kolk, Raymond Padmos & Alfons Hoekstra

  2. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK

    Tamás Józsa

Authors
  1. Claire Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Max van der Kolk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raymond Padmos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tamás Józsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alfons Hoekstra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claire Miller .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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

Miller, C., van der Kolk, M., Padmos, R., Józsa, T., Hoekstra, A. (2021). Uncertainty Quantification of Coupled 1D Arterial Blood Flow and 3D Tissue Perfusion Models Using the INSIST Framework. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77980-1_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77979-5

  • Online ISBN: 978-3-030-77980-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation