Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of Functionally Significant Stenosis

Brian S. Ko, James D. Cameron, Ravi K. Munnur, Dennis T.L. Wong, Yasuko Fujisawa, Takuya Sakaguchi, Kenji Hirohata, Jacqui Hislop-Jambrich, Shinichiro Fujimoto, Kazuhisa Takamura, Marcus Crossett, Michael Leung, Ahilan Kuganesan, Yuvaraj Malaiapan, Arthur Nasis, John Troupis, Ian T. Meredith, Sujith K. Seneviratne

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79 Citations (Scopus)


Objectives This study describes the feasibility and accuracy of a novel computed tomography (CT) fractional flow reserve (FFR) technique based on alternative boundary conditions. Background Techniques used to compute FFR based on images acquired from coronary computed tomography angiography (CTA) are described. Boundary conditions were typically determined by allometric scaling laws and assumptions regarding microvascular resistance. Alternatively, boundary conditions can be derived from the structural deformation of coronary lumen and aorta, although its accuracy remains unknown. Methods Forty-two patients (78 vessels) in a single institution prospectively underwent 320-detector coronary CTA and FFR. Deformation of coronary cross-sectional lumen and aorta, computed from coronary CTA images acquired over diastole, was used to determine the boundary conditions based on hierarchical Bayes modeling. CT-FFR was derived using a reduced order model performed using a standard desktop computer and dedicated software. First, 12 patients (20 vessels) formed the derivation cohort to determine optimal CT-FFR threshold with which to detect functional stenosis, defined as FFR of ≤0.8, which was validated in the subsequent 30 patients (58 vessels). Results Derivation cohort results demonstrated optimal threshold for CT-FFR was 0.8 with 67% sensitivity and 91% specificity. In the validation cohort, CT-FFR was successfully computed in 56 of 58 vessels (97%). Compared with coronary CTA, CT-FFR at ≤0.8 demonstrated a higher specificity (87% vs. 74%, respectively) and positive predictive value (74% vs. 60%, respectively), with comparable sensitivity (78% vs. 79%, respectively), negative predictive value (89% vs. 88%, respectively), and accuracy (area under the curve: 0.88 vs. 0.77, respectively; p = 0.22). Based on Bland-Altman analysis, mean intraobserver and interobserver variability values for CT-FFR were, respectively, −0.02 ± 0.05 (95% limits of agreement: −0.12 to 0.08) and 0.03 ± 0.06 (95% limits: 0.07 to 0.19). Mean time per patient for CT-FFR analysis was 27.07 ± 7.54 min. Conclusions CT-FFR based on alternative boundary conditions and reduced-order fluid model is feasible, highly reproducible, and may be accurate in detecting FFR ≤ 0.8. It requires a short processing time and can be completed at point-of-care. Further validation is required in large prospective multicenter settings.

Original languageEnglish
Pages (from-to)663-673
Number of pages11
JournalJACC: Cardiovascular Imaging
Issue number6
Publication statusPublished - 1 Jun 2017


  • computed tomography
  • coronary disease
  • fractional flow reserve
  • imaging
  • ischemia
  • quantitative coronary angiography

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