Validation of Numerical Simulations of Thoracic Aorta Hemodynamics: Comparison with In Vivo Measurements and Stochastic Sensitivity Analysis

Abstract

Purpose

Computational fluid dynamics (CFD) and 4D-flow magnetic resonance imaging (MRI) are synergically used for the simulation and the analysis of the flow in a patient-specific geometry of a healthy thoracic aorta.

Methods

CFD simulations are carried out through the open-source code SimVascular. The MRI data are used, first, to provide patient-specific boundary conditions. In particular, the experimentally acquired flow rate waveform is imposed at the inlet, while at the outlets the RCR parameters of the Windkessel model are tuned in order to match the experimentally measured fractions of flow rate exiting each domain outlet during an entire cardiac cycle. Then, the MRI data are used to validate the results of the hemodynamic simulations. As expected, with a rigid-wall model the computed flow rate waveforms at the outlets do not show the time lag respect to the inlet waveform conversely found in MRI data. We therefore evaluate the effect of wall compliance by using a linear elastic model with homogeneous and isotropic properties and changing the value of the Young’s modulus. A stochastic analysis based on the polynomial chaos approach is adopted, which allows continuous response surfaces to be obtained in the parameter space starting from a few deterministic simulations.

Results

The flow rate waveform can be accurately reproduced by the compliant simulations in the ascending aorta; on the other hand, in the aortic arch and in the descending aorta, the experimental time delay can be matched with low values of the Young’s modulus, close to the average value estimated from experiments. However, by decreasing the Young’s modulus the underestimation of the peak flow rate becomes more significant. As for the velocity maps, we found a generally good qualitative agreement of simulations with MRI data. The main difference is that the simulations overestimate the extent of reverse flow regions or predict reverse flow when it is absent in the experimental data. Finally, a significant sensitivity to wall compliance of instantaneous shear stresses during large part of the cardiac cycle period is observed; the variability of the time-averaged wall shear stresses remains however very low.

Conclusions

In summary, a successful integration of hemodynamic simulations and of MRI data for a patient-specific simulation has been shown. The wall compliance seems to have a significant impact on the numerical predictions; a larger wall elasticity generally improves the agreement with experimental data.

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Acknowledgments

The authors are grateful to Pau Simarro for his precious contribution in carrying out the numerical simulations.

Funding

No funding was received.

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Correspondence to Alessandro Mariotti.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. No animal studies were carried out for this study.

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Informed consent was obtained from all individual participants included in the study.

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Associate Editors Dr. David A. Steinman, Dr. Francesco Migliavacca, and Dr. Ajit P. Yoganathan oversaw the review of this article.

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Boccadifuoco, A., Mariotti, A., Capellini, K. et al. Validation of Numerical Simulations of Thoracic Aorta Hemodynamics: Comparison with In Vivo Measurements and Stochastic Sensitivity Analysis. Cardiovasc Eng Tech 9, 688–706 (2018). https://doi.org/10.1007/s13239-018-00387-x

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Keywords

  • Aorta
  • Computational fluid dynamics
  • Magnetic resonance imaging
  • Validation
  • Polynomial chaos expansion