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Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI

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Abstract

Purpose

Variations in the vessel radius of segmented surfaces of intracranial aneurysms significantly influence the fluid velocities given by computer simulations. It is important to generate models that capture the effect of these variations in order to have a better interpretation of the numerically predicted hemodynamics. Also, it is highly relevant to develop methods that combine experimental observations with uncertainty modeling to get a closer approximation to the blood flow behavior.

Methods

This work applies polynomial chaos expansion to model the effect of geometric uncertainties on the simulated fluid velocities of intracranial aneurysms. The radius of the vessel is defined as the uncertainty variable. Proper orthogonal decomposition is applied to characterize the solution space of fluid velocities. Next, a process of projecting the 4D-Flow MRI velocities on the basis vectors followed by coefficient mapping using generalized dynamic mode decomposition enables the merging of 4D-Flow MRI with the uncertainty propagated fluid velocities.

Results

Polynomial chaos expansion propagates the fluid velocities with an error of 2% in velocity magnitude relative to computer simulations. Also, the bifurcation region (or impingement location) shows a standard deviation of 0.17 m/s (since an available reported variance in the vessel radius is adopted to model the uncertainty, the expected standard deviation may be different). Numerical phantom experiments indicate that the proposed approach reconstructs the fluid velocities with 0.3% relative error in presence of geometric uncertainties.

Conclusion

Polynomial chaos expansion is an effective approach to propagate the effect of the uncertainty variable in the blood flow velocities of intracranial aneurysms. Merging 4D-Flow MRI and uncertainty propagated fluid velocities leads to more realistic flow trends relative to ignoring the uncertainty in the vessel radius.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA

    Isaac Perez-Raya, Mojtaba F. Fathi & Roshan M. D’Souza

  2. Department of Electrical and Computer Engineering, New York Institute of Technology, Old Westbury, NY, 11568, USA

    Ahmadreza Baghaie

  3. Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, 53226, USA

    Raphael Sacho

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  1. Isaac Perez-Raya
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Perez-Raya, I., Fathi, M.F., Baghaie, A. et al. Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI. Cardiovasc Eng Tech 12, 127–143 (2021). https://doi.org/10.1007/s13239-020-00511-w

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