Reduced-order modeling of blood flow for noninvasive functional evaluation of coronary artery disease

  • Stefano BuosoEmail author
  • Andrea Manzoni
  • Hatem Alkadhi
  • André Plass
  • Alfio Quarteroni
  • Vartan KurtcuogluEmail author
Original Paper


We present a novel computational approach, based on a parametrized reduced-order model, for accelerating the calculation of pressure drop along blood vessels. Vessel lumina are defined by a geometric parametrization using the discrete empirical interpolation method on control points located on the surface of the vessel. Hemodynamics are then computed using a reduced-order representation of the parametrized three-dimensional unsteady Navier–Stokes and continuity equations. The reduced-order model is based on an offline–online splitting of the solution process, and on the projection of a finite volume full-order model on a low-dimensionality subspace generated by proper orthogonal decomposition of pressure and velocity fields. The algebraic operators of the hemodynamic equations are assembled efficiently during the online phase using the discrete empirical interpolation method. Our results show that with this approach calculations can be sped up by a factor of about 25 compared to the conventional full-order model, while maintaining prediction errors within the uncertainty limits of invasive clinical measurement of pressure drop. This is of importance for a clinically viable implementation of noninvasive, medical imaging-based computation of fractional flow reserve.


FFR Coronary artery disease Computational fluid dynamics Finite volumes method Discrete empirical interpolation method Navier–Stokes Proper orthogonal decomposition Reduced basis method Reduced-order modeling 



The authors acknowledge the financial support of Mr. Joe Clark, the Swiss National Science Foundation through NCCR Kidney.CH, and the University of Zurich through the Forschungskredit Postdoc Fellowship (FK-18-043).

Compliance with ethical standards

Conflict of interest

A patent application covering parts of the technology described in the manuscript has been filed.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Interface Group, Institute of PhysiologyUniversity of ZurichZurichSwitzerland
  2. 2.Institute of Diagnostic and Interventional Radiology, University Hospital ZurichZurichSwitzerland
  3. 3.Chair of Modeling and Scientific Computing, Mathematics Institute of Computational Science and EngineeringÉcole Fédérale Polytechnique de LausanneLausanneSwitzerland
  4. 4.Clinic for Cardiovascular Surgery, University Hospital ZurichZurichSwitzerland
  5. 5.National Center of Competence in Research, Kidney.CHZurichSwitzerland
  6. 6.Zurich Center for Integrative Human PhysiologyUniversity of ZurichZurichSwitzerland

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