Accuracy and reproducibility of fast fractional flow reserve computation from invasive coronary angiography

Abstract

Fractional flow reserve (FFR) guided percutaneous coronary intervention (PCI) is associated with favourable outcome compared with revascularization based on angiographic stenosis severity alone. The feasibility of the new image-based quantitative flow ratio (QFR) assessed from 3D quantitative coronary angiography (QCA) and thrombolysis in myocardial infarction (TIMI) frame count using three different flow models has been reported recently. The aim of the current study was to assess the accuracy, and in particular, the reproducibility of these three QFR techniques when compared with invasive FFR. QFR was derived (1) from adenosine induced hyperaemic coronary angiography images (adenosine-flow QFR [aQFR]), (2) from non-hyperemic images (contrast-flow QFR [cQFR]) and (3) using a fixed empiric hyperaemic flow [fixed-flow QFR (fQFR)]. The three QFR values were calculated in 17 patients who prospectively underwent invasive FFR measurement in 20 vessels. Two independent observers performed the QFR analyses. Mean difference, standard deviation and 95% limits of agreement (LOA) between invasive FFR and aQFR, cQFR and fQFR for observer 1 were: 0.01 ± 0.04 (95% LOA: -0.07; 0.10), 0.01 ± 0.05 (95% LOA: -0.08; 0.10), 0.01 ± 0.04 (95% LOA: -0.06; 0.08) and for observer 2: 0.00 ± 0.03 (95% LOA: -0.06; 0.07), -0.01 ± 0.03 (95% LOA: -0.07; 0.05), 0.00 ± 0.03 (95% LOA: -0.06; 0.05). Values between the 2 observers were (to assess reproducibility) for aQFR: 0.01 ± 0.04 (95% LOA: -0.07; 0.09), for cQFR: 0.02 ± 0.04 (95% LOA: -0.06; 0.09) and for fQFR: 0.01 ± 0.05 (95% LOA: -0.07; 0.10). In a small number of patients we showed good accuracy of three QFR techniques (aQFR, cQFR and fQFR) to predict invasive FFR. Furthermore, good inter-observer agreement of the QFR values was observed between two independent observers.

Keywords: Computational fluid dynamics; Fractional flow reserve; Quantitative coronary angiography.

Conflict of interest statement

Conflict of interest

J. H. C. Reiber is the Chief Executive Officer of Medis; and has a parttime appointment at LUMC as Professor of Medical Imaging. G. Koning is an employee at Medis.

Disclosure

A. J. H. A. Scholte received consulting fees from Toshiba Medical Systems and GE Healthcare.

Figures

Fig. 1

Example of computation of cQFR from 3D QCA and TIMI frame count. a, b 2 baseline, end-diastolic angiographic projections >25° apart in viewing angle. The red and green circle (asterisk) represent anatomical landmarks that serve as reference points in both projections for automated correction of angiographic system distortions. c, d After selection of baseline projections (a, b), the same projections (c, d) are used for automated lumen and vessel wall contour detection. Yellow represents coronary artery plaques (atherosclerosis). e–h: TIMI frame counting performed on one of the two baseline projections (a, c). The contrast bolus injection reached the proximal part of the quantified vessel segment at frame 11 (e). The distal part of the quantified segment was reached at frame 17 (h). The red line indicates the frontline of the contrast bolus. i The diameters of the vessel derived from the two projections. The green lines represent the proximal and distal part of the most severe coronary artery lesion and the purple line indicates the site of maximum stenosis severity. j 3D reconstruction of the coronary artery. The colours represent the decreasing QFR alongside the coronary artery. The cQFR at the most distal part of the analysed segment was 0.85; the invasively measured FFR was 0.84 at the same location. k 2D display of the pressure drop alongside the coronary artery

Fig. 2

Correlation and Bland–Altman analysis between aQFR and FFR data

Fig. 3

Correlation and Bland–Altman analysis between cQFR and FFR data

Fig. 4

Correlation and Bland–Altman analysis between fQFR and FFR data

Fig. 5

Inter-observer variability between the two observers