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

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Computational Science – ICCS 2021 (ICCS 2021)

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.

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Acknowledgements

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

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Correspondence to Claire Miller .

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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

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

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  • Online ISBN: 978-3-030-77980-1

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