European Radiology

, Volume 29, Issue 4, pp 2117–2126 | Cite as

Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve

  • Panagiotis K. Siogkas
  • Constantinos D. AnagnostopoulosEmail author
  • Riccardo Liga
  • Themis P. Exarchos
  • Antonis I. Sakellarios
  • George Rigas
  • Arthur J. H. A. Scholte
  • M. I. Papafaklis
  • Dimitra Loggitsi
  • Gualtiero Pelosi
  • Oberdan Parodi
  • Teemu Maaniitty
  • Lampros K. Michalis
  • Juhani Knuuti
  • Danilo Neglia
  • Dimitrios I. Fotiadis
Computed Tomography



Application of computational fluid dynamics (CFD) to three-dimensional CTCA datasets has been shown to provide accurate assessment of the hemodynamic significance of a coronary lesion. We aim to test the feasibility of calculating a novel CTCA-based virtual functional assessment index (vFAI) of coronary stenoses > 30% and ≤ 90% by using an automated in-house-developed software and to evaluate its efficacy as compared to the invasively measured fractional flow reserve (FFR).

Methods and results

In 63 patients with chest pain symptoms and intermediate (20–90%) pre-test likelihood of coronary artery disease undergoing CTCA and invasive coronary angiography with FFR measurement, vFAI calculations were performed after 3D reconstruction of the coronary vessels and flow simulations using the finite element method. A total of 74 vessels were analyzed. Mean CTCA processing time was 25(± 10) min. There was a strong correlation between vFAI and FFR, (R = 0.93, p < 0.001) and a very good agreement between the two parameters by the Bland–Altman method of analysis. The mean difference of measurements from the two methods was 0.03 (SD = 0.033), indicating a small systematic overestimation of the FFR by vFAI. Using a receiver-operating characteristic curve analysis, the optimal vFAI cutoff value for identifying an FFR threshold of ≤ 0.8 was ≤ 0.82 (95% CI 0.81 to 0.88).


vFAI can be effectively derived from the application of computational fluid dynamics to three-dimensional CTCA datasets. In patients with coronary stenosis severity > 30% and ≤ 90%, vFAI performs well against FFR and may efficiently distinguish between hemodynamically significant from non-significant lesions.

Key Points

  • Virtual functional assessment index (vFAI) can be effectively derived from 3D CTCA datasets.

  • In patients with coronary stenoses severity > 30% and ≤ 90%, vFAI performs well against FFR.

  • vFAI may efficiently distinguish between functionally significant from non-significant lesions.


Coronary artery disease Myocardial fractional flow reserve Computed tomography angiography 



Coronary artery calcium score


Coronary artery disease


Computational fluid dynamics


Computed tomography coronary angiography


Fractional flow reserve


Invasive coronary angiography


Virtual functional assessment index



This work was supported in part by European Union FP7-CP-FP506 2007 (grant no. 222915) (EVINCI study) and in part by European Union’s Horizon 2020 research and innovation program under grant agreement no. 689068 (SMARTool study).

Compliance with ethical standards


The scientific guarantor of this publication is Constantinos D. Anagnostopoulos.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

In the context of the EVINCI study (Neglia D et al Circ Cardiovasc Imaging. 2015 Mar;8(3). pii: e002179. doi:, ethical approval was provided by each participating center and all subjects gave written informed consent. For the present study investigating anonymized imaging data, informed consent was waived.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• multicenter study


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Panagiotis K. Siogkas
    • 1
  • Constantinos D. Anagnostopoulos
    • 2
    Email author
  • Riccardo Liga
    • 3
    • 4
  • Themis P. Exarchos
    • 5
  • Antonis I. Sakellarios
    • 5
  • George Rigas
    • 5
  • Arthur J. H. A. Scholte
    • 6
  • M. I. Papafaklis
    • 7
  • Dimitra Loggitsi
    • 8
  • Gualtiero Pelosi
    • 9
  • Oberdan Parodi
    • 9
  • Teemu Maaniitty
    • 10
  • Lampros K. Michalis
    • 7
  • Juhani Knuuti
    • 10
  • Danilo Neglia
    • 9
  • Dimitrios I. Fotiadis
    • 1
    • 5
    • 7
  1. 1.Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and EngineeringUniversity of IoanninaIoanninaGreece
  2. 2.Center for Experimental Surgery, Clinical and Translational Research, Biomedical Research FoundationAcademy of AthensAthensGreece
  3. 3.Cardio-Thoracic and Vascular DepartmentUniversity Hospital of PisaPisaItaly
  4. 4.Department of Nuclear MedicineUniversity Hospital ZurichZürichSwitzerland
  5. 5.Biomedical Research Institute – FORTH, GR 45110 IoanninaIoanninaGreece
  6. 6.Department of CardiologyLeiden University Medical CenterLeidenThe Netherlands
  7. 7.Michaelideion Cardiac Center, Dept. of Cardiology in Medical SchoolUniversity of IoanninaIoanninaGreece
  8. 8.CT & MRI Department Hygeia-Mitera HospitalsAthensGreece
  9. 9.Fondazione Toscana G. Monasterio and CNR Institute of Clinical PhysiologyPisaItaly
  10. 10.Turku PET CentreUniversity of Turku and Turku University HospitalTurkuFinland

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