Cardiovascular Engineering and Technology

, Volume 9, Issue 4, pp 597–622 | Cite as

Uncertainty Quantification and Sensitivity Analysis for Computational FFR Estimation in Stable Coronary Artery Disease

  • Fredrik E. FossanEmail author
  • Jacob Sturdy
  • Lucas O. Müller
  • Andreas Strand
  • Anders T. Bråten
  • Arve Jørgensen
  • Rune Wiseth
  • Leif R. Hellevik



The main objectives of this study are to validate a reduced-order model for the estimation of the fractional flow reserve (FFR) index based on blood flow simulations that incorporate clinical imaging and patient-specific characteristics, and to assess the uncertainty of FFR predictions with respect to input data on a per patient basis.


We consider 13 patients with symptoms of stable coronary artery disease for which 24 invasive FFR measurements are available. We perform an extensive sensitivity analysis on the parameters related to the construction of a reduced-order (hybrid 1D–0D) model for FFR predictions. Next we define an optimal setting by comparing reduced-order model predictions with solutions based on the 3D incompressible Navier–Stokes equations. Finally, we characterize prediction uncertainty with respect to input data and identify the most influential inputs by means of sensitivity analysis.


Agreement between FFR computed by the reduced-order model and by the full 3D model was satisfactory, with a bias (\(\text{FFR} _{{\text {3D}}}- \text{FFR} _{{\text {1D}}{-}{\text {0D}}}\)) of \(-\,0.03\,(\pm\, 0.03)\) at the 24 measured locations. Moreover, the uncertainty related to the factor by which peripheral resistance is reduced from baseline to hyperemic conditions proved to be the most influential parameter for FFR predictions, whereas uncertainty in stenosis geometry had greater effect in cases with low FFR.


Model errors related to solving a simplified reduced-order model rather than a full 3D problem were small compared with uncertainty related to input data. Improved measurement of coronary blood flow has the potential to reduce uncertainty in computational FFR predictions significantly.


Computational FFR Uncertainty quantification Model complexity Total uncertainty 



This work was partially supported by NTNU Health (Strategic Research Area at the Norwegian University of Science and Technology) and by The Liaison Committee for Education, Research and Innovation in Central Norway. Computational resources in Norwegian HPC Infrastructure were granted by the Norwegian Research Council by Project Nr. NN9545K. LRH was partly funded by a Peder Sather Grant: Mainstreaming Sensitivity Analysis And Uncertainty Auditing.

Conflict of interest

There are no conflicts of interest.

Ethical Approval

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.

Informed Consent

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


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Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  1. 1.Department of Structural EngineeringNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Clinic of CardiologySt. Olavs HospitalTrondheimNorway
  3. 3.Department of Circulation and Medical ImagingNorwegian University of Science and TechnologyTrondheimNorway
  4. 4.Department of Radiology and Nuclear MedicineSt. Olavs HospitalTrondheimNorway

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