Overview of Quantitative Flow Ratio and Optical Flow Ratio in the Assessment of Intermediate Coronary Lesions

Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) improves clinical outcome compared with angiography-guided PCI. Advances in computational technology have resulted in the development of solutions, enabling fast derivation of FFR from imaging data in the catheterization laboratory. The quantitative flow ratio is currently the most validated approach to derive FFR from invasive coronary angiography, while the optical flow ratio allows faster and more automation in FFR computation from intracoronary optical coherence tomography. The use of quantitative flow ratio and optical flow ratio has the potential for swift and safe identification of lesions that require revascularization, optimization of PCI, evaluation of plaque features, and virtual planning of PCI

Quantitative flow ratio-guided surgical intervention in symptomatic myocardial bridging

Background: Patients with myocardial bridging (MB) are associated with adverse cardiovascular events, but a decision to perform surgical intervention, especially for patients with systolic intermediate stenosis, is a difficult clinical issue. Fractional flow reserve (FFR) represents a novel method for the functional evaluation of coronary stenosis, but the relationship between FFR and MB remains controversial because of the cyclic dynamic stenosis of MB. Quantitative flow ratio (QFR) is a novel index allowing fast assessment of FFR from a diagnostic coronary angiography. This study aimed to investigate the relationship between QFR and MB patients and to further develop a prediction model of QFR-guided surgical intervention for these patients.Methods: Forty-five symptomatic lone MB patients who had undergone coronary angiography were consecutively enrolled in this study. MB was located in the middle of left anterior descending artery with intermediate stenosis during systole. The patients were retrospectively divided into a medical therapy group or a surgical therapy group. Systolic geometry based QFR (SG-QFR) and diastolic geometry based QFR (DG-QFR) were calculated based on three-dimensional quantitative coronary angiography and patient-specific flow velocity. Subsequently, time-averaged QFR (TA-QFR) is defined as the average of SG-QFR and DG-QFR.Results: Receiver operating characteristic curve analysis revealed that TA-QFR (AUC = 0.91; 95% CI: 0.79–0.98) was found to be the best pre-operative index for surgical intervention to MB, when compared with DG-QFR (AUC = 0.69; 95% CI: 0.53–0.82; difference: 0.22; 95% CI: 0.04–0.41; p = 0.02) and SG-QFR (AUC = 0.87; 95% CI: 0.74–0.95; difference: 0.04; 95% CI: 0.00–0.08; p = 0.03).Conclusions: TA-QFR improved the performance of functional evaluation in MB patients with intermediate stenosis during systole and is useful for guiding surgical intervention

Fusion of 3D QCA and IVUS/OCT

The combination/fusion of quantitative coronary angiography (QCA) and intravascular ultrasound (IVUS)/optical coherence tomography (OCT) depends to a great extend on the co-registration of X-ray angiography (XA) and IVUS/OCT. In this work a new and robust three-dimensional (3D) segmentation and registration approach is presented and validated. The approach starts with standard QCA of the vessel of interest in the two angiographic views (either biplane or two monoplane views). Next, the vessel of interest is reconstructed in 3D and registered with the corresponding IVUS/OCT pullback series by a distance mapping algorithm. The accuracy of the registration was retrospectively evaluated on 12 silicone phantoms with coronary stents implanted, and on 24 patients who underwent both coronary angiography and IVUS examinations of the left anterior descending artery. Stent borders or sidebranches were used as markers for the validation. While the most proximal marker was set as the baseline position for the distance mapping algorithm, the subsequent markers were used to evaluate the registration error. The correlation between the registration error and the distance from the evaluated marker to the baseline position was analyzed. The XA-IVUS registration error for the 12 phantoms was 0.03 ± 0.32 mm (P = 0.75). One OCT pullback series was excluded from the phantom study, since it did not cover the distal stent border. The XA-OCT registration error for the remaining 11 phantoms was 0.05 ± 0.25 mm (P = 0.49). For the in vivo validation, two patients were excluded due to insufficient image quality for the analysis. In total 78 sidebranches were identified from the remaining 22 patients and the registration error was evaluated on 56 markers. The registration error was 0.03 ± 0.45 mm (P = 0.67). The error was not correlated to the distance between the evaluated marker and the baseline position (P = 0.73). In conclusion, the new XA-IVUS/OCT co-registration approach is a straightforward and reliable solution to combine X-ray angiography and IVUS/OCT imaging for the assessment of the extent of coronary artery disease. It provides the interventional cardiologist with detailed information about vessel size and plaque size at every position along the vessel of interest, making this a suitable tool during the actual intervention

Reproducibility of quantitative flow ratio: An inter-core laboratory variability study

Background: Quantitative flow ratio (QFR) is a novel approach to derive fractional flow reserve (FFR) from coronary angiography. This study sought to evaluate the reproducibility of QFR when analyzed in independent core laboratories. Methods: All interrogated vessels in the FAVOR II China Study were separately analyzed using the AngioPlus system (Pulse medical imaging technology, Shanghai) by two independent core laboratories, following the same standard operation procedures. The analysts were blinded to the FFR values and online QFR values. For each interrogated vessel, two identical angiographic image runs were used by two core laboratories for QFR computation. In both core laboratories QFR was successfully obtained in 330 of 332 vessels, in which FFR was available in 328 vessels. Thus, 328 vessels ended in the present statistical analysis. Results: The mean difference in contrast-flow QFR between the two core laboratories was 0.004 ± 0.03 (p = 0.040), which was slightly smaller than that between the online analysis and the two core laboratories (0.01 ± 0.05, p < 0.001 and 0.01 ± 0.05, p = 0.038). The mean difference of QFR with re­spect to FFR were comparable between the two core laboratories (0.002 ± 0.06, p = 0.609, and 0.002 ± 0.06, p = 0.531). Receiver operating characteristic curve analysis showed that diagnostic accuracies of QFR analyzed by the two core laboratories were both excellent (area under the curve: 0.970 vs. 0.963, p = 0.142), when using FFR as the reference standard. Conclusions: The present study showed good inter-core laboratory reproducibility of QFR in assessing functionally-significant stenosis. It suggests that QFR analyses can be carried out in different core labo­ratories if, and only if, highly standardized conditions are maintained

Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence

Background: Coronary flow reserve (CFR) has prognostic value in patients with coronary artery disease. However, its measurement is complex, and automatic methods for CFR computation are scarcely available. We developed an automatic method for CFR computation based on coronary angiography and assessed its feasibility. Methods: Coronary angiographies from the Corelab database were annotated by experienced analysts. A convolutional neural network (CNN) model was trained for automatic segmentation of the main coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold cross-validation. Subsequently, the CNN model was implemented into a prototype software package for automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angiographies performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and its agreement with the manual computational method based on frame count (CFRmanual). Results: Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was successfully computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was 18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and CFRmanual, with a mean difference of 0.12 ± 0.53. Conclusions: Automatic computation of the CFR based on coronary angiography is feasible. This method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess microcirculatory function

Is it safe to implant bioresorbable scaffolds in ostial side-branch lesions? Impact of 'neo-carina' formation on main-branch flow pattern. Longitudinal clinical observations

Formation of a 'neo-carina' has been reported after bioresorbable vascular scaffolds (BVS) implantation over side-branches. However, as this 'neo-carina' could protrude into the main-branch, its hemodynamic impact remains unknown. We present two cases of BVS implantation for ostial side-branch lesions, and investigate the flow patterns at follow-up and their potential impact. Computational fluid dynamics analysis was performed, using a 3D mesh created by fusion of 3-dimensional angiogram with optical coherence tomography images. In our first case, mild disturbances were seen when 'neo-carina' did not protrude perpendicularly into the main branch. In the second case, extensive flow re-distribution was observed due to a more pronounced protrusion of the 'neo-carina'. Importantly, these areas of hemodynamic disturbance were observed together with lumen narrowing in a non-stenotic vessel segment. Our case observations highlight the importance of investigating the hemodynamic consequences of BVS implantation in bifurcation lesions and illustrate a novel method to do so invivo

Optimal diagnostic approach for using CT-derived quantitative flow ratio in patients with stenosis on coronary computed tomography angiography

Background: Coronary computed tomography angiography (CCTA)-derived quantitative flow ratio (CT-QFR) is an on-site non-invasive technique estimating invasive fractional flow reserve (FFR). This study assesses the diagnostic performance of using most distal CT-QFR versus lesion-specific CT-QFR approach for identifying hemodynamically obstructive coronary artery disease (CAD).Methods: Prospectively enrolled de novo chest pain patients (n ​= ​445) with ≥50 ​% visual diameter stenosis on CCTA were referred for invasive evaluation. On-site CT-QFR was analyzed post-hoc blinded to angiographic data and obtained as both most distal (MD-QFR) and lesion-specific CT-QFR (LS-QFR). Abnormal CT-QFR was defined as ≤0.80. Hemodynamically obstructive CAD was defined as invasive FFR ≤0.80 or ≥70 ​% diameter stenosis by 3D-quantitative coronary angiography.Results: In total 404/445 patients had paired CT-QFR and invasive analyses of whom 149/404 (37 ​%) had hemodynamically obstructive CAD. MD-QFR and LS-QFR classified 188 (47 ​%) and 165 (41 ​%) patients as abnormal, respectively. Areas under the receiver-operating characteristic curve for MD-QFR was 0.83 vs. 0.85 for LS-QFR, p ​= ​0.01. Sensitivities for MD-QFR and LS-QFR were 80 ​% (95%CI: 73-86) vs. 77 ​% (95%CI: 69-83), p ​= ​0.03, respectively, and specificities were 73 ​% (95%CI: 67-78) vs. 80 ​% (95%CI: 75-85), p ​< ​0.01, respectively. Positive predictive values for MD-QFR and LS-QFR were 63 ​% vs. 69 ​%, p ​< ​0.01, respectively, and negative predictive values for MD-QFR and LS-QFR were 86 ​% vs. 85 ​%, p ​= ​0.39, respectively).Conclusion: Using a lesion-specific CT-QFR approach has superior discrimination of hemodynamically obstructive CAD compared to a most distal CT-QFR approach. CT-QFR identified most cases of hemodynamically obstructive CAD while a normal CT-QFR excluded hemodynamically obstructive CAD in the majority of patients

Impact of calcification on Murray law-based quantitative flow ratio for physiological assessment of intermediate coronary stenoses

Background: To investigate the influence of coronary calcification on the diagnostic performance of Murray law-based quantitative flow ratio (μQFR) in identifying hemodynamically significant coronary lesions referenced to fractional flow reserve (FFR). Methods: A total of 571 intermediate lesions from 534 consecutive patients (66.1 ± 10.0 years, 67.2% males) who underwent coronary angiography and simultaneous FFR measurement were included. Calcific deposits were graded by angiography as none or mild (spots), moderate (involving ≤ 50% of the reference vessel diameter), and severe (> 50%). Performance of μQFR to detect functional ischemia (FFR ≤ 0.80) was evaluated, including diagnostic parameters and areas under the receiver-operating curves (AUCs). Results: The discrimination of ischemia by μQFR was comparable between none/mild and moderate/severe calcification (AUC: 0.91 [95% confidence interval: 0.88–0.93] vs. 0.87 [95% confidence interval: 0.78–0.94]; p = 0.442). No statistically significant difference was observed for μQFR between the two categories in sensitivity (0.70 vs. 0.69, p = 0.861) and specificity (0.94 vs. 0.90, p = 0.192). Moreover, μQFR showed significantly higher AUCs than quantitative coronary angiographic diameter stenosis in both vessels with none/mild (0.91 vs. 0.78, p < 0.001) and moderate/severe calcification (0.87 vs. 0.69, p < 0.001). By multivariable analysis, there was no association between calcification and μQFR-FFR discordance (adjusted odds ratio: 1.529, 95% confidence interval: 0.788–2.968, p = 0.210) after adjustment for other confounding factors. Conclusions: μQFR demonstrated robust and superior diagnostic performance for lesion-specific ischemia compared with angiography alone regardless of coronary calcification