Computationally simulated fractional flow reserve from coronary computed tomography angiography based on fractional myocardial mass

  • Huan Han
  • Yong Gyun Bae
  • Seung Tae Hwang
  • Hyung-Yoon Kim
  • Il Park
  • Sung-Mok Kim
  • Yeonhyeon Choe
  • Young-June MoonEmail author
  • Jin-Ho ChoiEmail author
Original Paper


Computed tomography angiography (CCTA)-based calculations of fractional flow reserve (FFR) can improve the diagnostic performance of CCTA for physiologically significant stenosis but the computational resource requirements are high. This study aimed at establishing a simple and efficient algorithm for computing simulated FFR (S-FFR). A total of 107 patients who underwent CCTA and invasive FFR measurements were enrolled in the study. S-FFR was calculated using 145 evaluable coronary arteries with off-the-shelf softwares. FFR ≤ 0.80 was a reference threshold for diagnostic performance of diameter stenosis (DS) ≥ 50%, DS ≥ 70%, or S-FFR ≤ 0.80. FFR ≤ 0.80 was identified in 78 vessels (54%). In per-vessel analysis, S-FFR showed good correlation (r = 0.83) and agreement (mean difference = 0.02 ± 0.08) with FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of S-FFR ≤ 0.80 for FFR ≤ 0.80 were 84%, 92%, 92%, 83%, and 88%, respectively. S-FFR ≤ 0.80 showed much higher predictive performance for FFR ≤ 0.80 compared with DS ≥ 50% or DS ≥ 70% (c-statistics = 0.92 vs. 0.58 or 0.65, p < 0.001, all). The classification agreement between FFR and S-FFR was > 80% when the average of FFR and S-FFR was < 0.76 or > 0.86. Per-patient analysis showed consistent results. In this study, a simple and computationally efficient simulated FFR (S-FFR) algorithm is designed and tested using non-proprietary off-the-shelf software. This algorithm may expand the accessibility of clinical applications for non-invasive coronary physiology study.


Coronary circulation Computational coronary physiology Computed tomography 





Coronary angiography


Computational flow dynamics


Coronary computed tomography angiography


Diameter stenosis


Fractional flow reserve


Integrated discrimination improvement


Negative predictive value


Net reclassification improvement


Positive predictive value


Receive-operating characteristics



We thank Seon A Jeong and So Hyeon Park for their excellent and devoted contributions.


This study was funded by This study was supported by Korean Society of Circulation Grant (201301-01), Korean Society of Interventional Cardiology Grant [2014-1], Samsung Biomedical Research Institute Grant [GL1B33211], Samsung Medical Center Heart Vascular and Stroke Institute Clinical Research Project (OTC1601861), and National Research Foundation of Korea (2017R1A2B3010918).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict 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

Samsung Medical Center institutional review board approved this study investigating anonymized image and physiological data and informed consent was waived.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Computational Fluid Dynamics and Acoustics Laboratory, School of Mechanical EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Department of MedicineChonnam National University Medical SchoolGwangjuRepublic of Korea
  3. 3.Department of Medicine, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea
  4. 4.Department of Radiology, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea
  5. 5.Department of Emergency Medicine, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea

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