Automatic coronary blood flow computation: validation in quantitative flow ratio from coronary angiography

Zhang, Yimin; Zhang, Su; Westra, Jelmer; Ding, Daixin; Zhao, Qiuyang; Yang, Jing; Sun, Zhongwei; Huang, Jiayue; Pu, Jun; Xu, Bo; Tu, Shengxian

doi:10.1007/s10554-018-1506-y

Cited by 22 publications

(18 citation statements)

References 21 publications

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“…Nevertheless, previous studies demonstrated that such variation in the contrast flow velocity had negligible impact on the diagnostic accuracy of QFR. 13 Notably, the reconstruction of the step-down reference vessel size also improved the diagnostic concordance between QCA-derived DS% and FFR. In the FAVOR II China study, DS% by conventional QCA-derived DS% with linear tapering of the reference diameter function had an accuracy of only 64.0% in predicting FFR ≤0.80.…”

Section: Discussionmentioning

confidence: 91%

“…Finally, the quantification of contrast flow velocity in the computation of μQFR based on a single angiographic view might indeed be influenced by vessel foreshortening in individual patients. Nevertheless, previous studies demonstrated that such variation in the contrast flow velocity had negligible impact on the diagnostic accuracy of QFR 13 …”

Section: Discussionmentioning

confidence: 94%

“…Computation of μQFR was performed using a prototype software (AngioPlus Core, Pulse Medical Imaging Technology, Shanghai, China) by an experienced analyst who was blinded to FFR data. The computation of μQFR can be summarized with a few steps: (a) delineation of the interrogated epicardial coronary artery during contrast injection and calculation of contrast flow velocity based on the centerline length divided by the contrast dye-filing time; 13 (b) selection of the analysis frame with sharp lumen contour at the stenotic segment as key frame; (c) delineation of the lumen contour of the interrogated vessel and its side branches with diameters of ≥1.0 mm on the key frame; (d) reconstruction of reference diameter function with stepdown size across bifurcations according to Murray fractal law; 14,15 (e) modeling of hyperemic flow velocity based on the contrast flow velocity and calculation of pressure drop based on fluid dynamics Equations, 16 assuming a blood density of 1,060 kg/m 3 and viscosity of 0.0035 kg/(mÁs). Specifically, the pressure loss caused by the frictional loss along the lesion entrance and stenotic segment as well as the inertial loss stemming from the sudden expansion of the flow as it emerged from the stenosis were calculated, based on the stenosis geometry and the hyperemic flow rate.…”

Section: Analysis Of μQfr and Quantitative Coronary Angiographymentioning

confidence: 99%

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Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law

Ding

Chang³

et al. 2021

Cathet Cardio Intervent

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117

138

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Objectives We aimed to evaluate the diagnostic accuracy of computation of fractional flow reserve (FFR) from a single angiographic view in patients with intermediate coronary stenosis. Background Computation of quantitative flow ratio (QFR) from a single angiographic view might increase the feasibility of routine use of computational FFR. In addition, current QFR solutions assume a linear tapering of the reference vessel size, which might decrease the diagnostic accuracy in the presence of the physiologically significant bifurcation lesions. Methods An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of major epicardial coronary arteries including their side branches. A step‐down reference diameter function was reconstructed based on the Murray bifurcation fractal law and used for QFR computation. Validation of this Murray law‐based QFR (μQFR) was performed on the FAVOR II China study population. The μQFR was computed separately in two angiographic projections, starting with the one with optimal angiographic image quality. Hemodynamically significant coronary stenosis was defined by pressure wire‐derived FFR ≤0.80. Results The μQFR was successfully computed in all 330 vessels of 306 patients. There was excellent correlation (r = 0.90, p < .001) and agreement (mean difference = 0.00 ± 0.05, p = .378) between μQFR and FFR. The vessel‐level diagnostic accuracy for μQFR to identify hemodynamically significant stenosis was 93.0% (95% CI: 90.3 to 95.8%), with sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio of 87.5% (95% CI: 80.2 to 92.8%), 96.2% (95% CI: 92.6 to 98.3%), 92.9% (95% CI: 86.5 to 96.9%), 93.1% (95% CI: 88.9 to 96.1%), 23.0 (95% CI: 11.6 to 45.5), 0.13 (95% CI: 0.08 to 0.20), respectively. Use of suboptimal angiographic image view slightly decreased the diagnostic accuracy of μQFR (AUC = 0.97 versus 0.92, difference = 0.05, p < .001). Intra‐ and inter‐observer variability for μQFR computation was 0.00 ± 0.03, and 0.00 ± 0.03, respectively. Average analysis time for μQFR was 67 ± 22 s. Conclusions Computation of μQFR from a single angiographic view has high feasibility and excellent diagnostic accuracy in identifying hemodynamically significant coronary stenosis. The short analysis time and good reproducibility of μQFR bear potential of wider adoption of physiological assessment in the catheterization laboratory.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 94%

Section: Analysis Of μQfr and Quantitative Coronary Angiographymentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law

Ding

Chang³

et al. 2021

Cathet Cardio Intervent

Self Cite

117

138

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show abstract

“…Zhang et al validated a new approach for automatic coronary blood flow computation from coronary angiograms to further automate the Quantitative Flow Ratio (QFR) approach for the assessment of the physiologic significance of coronary vessels [8]. A total of 328 vessels with paired FFR data from the FAVOR II China study was used for the validation.…”

Section: X-ray Imagingmentioning

confidence: 99%

Cardiovascular imaging 2019 in the International Journal of Cardiovascular Imaging

Reiber

Pereira

Dallan

et al. 2020

Int J Cardiovasc Imaging

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“…In several observational studies comparing QFR and FFR, common clinical conditions were excluded from the analysis (such as presence of complex lesions, bifurcations, and medical history of recent myocardial infarction or coronary artery bypass grafting); thus it is difficult to estimate the functional significance in these settings [10]. Recently, a novel automatic method of QFR computation provides good diagnostic accuracy in determining the functional significance of coronary stenosis, which may accelerate the application of QFR in daily practice [39].…”

Section: Combinedmentioning

confidence: 99%

Meta-Analysis of Diagnostic Performance of Contrast-Fractional Flow Reserve versus Quantitative Flow Ratio for Functional Assessment of Coronary Stenoses

Zhang

Guo

2020

Journal of Interventional Cardiology

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Background. Use of the fractional flow reserve (FFR) technique is recommended to evaluate coronary stenosis severity and guide revascularization. However, its high cost, time to administer, and the side effects of adenosine reduce its clinical utility. Two novel adenosine-free indices, contrast-FFR (cFFR) and quantitative flow ratio (QFR), can simplify the functional evaluation of coronary stenosis. This study aimed to analyze the diagnostic performance of cFFR and QFR using FFR as a reference index. Methods. We conducted a systematic review and meta-analysis of observational studies in which cFFR or QFR was compared to FFR. A bivariate model was applied to pool diagnostic parameters. Cochran’s Q test and the I2 index were used to assess heterogeneity and identify the potential source of heterogeneity by metaregression and sensitivity analysis. Results. Overall, 2220 and 3000 coronary lesions from 20 studies were evaluated by cFFR and QFR, respectively. The pooled sensitivity and specificity were 0.87 (95% CI: 0.81, 0.91) and 0.92 (95% CI: 0.88, 0.94) for cFFR and 0.87 (95% CI: 0.82, 0.91) and 0.91 (95% CI: 0.87, 0.93) for QFR, respectively. No statistical significance of sensitivity and specificity for cFFR and QFR were observed in the bivariate analysis (P=0.8406 and 0.4397, resp.). The area under summary receiver-operating curve of cFFR and QFR was 0.95 (95% CI: 0.93, 0.97) for cFFR and 0.95 (95% CI: 0.93, 0.97). Conclusion. Both cFFR and QFR have good diagnostic performance in detecting functional severity of coronary arteries and showed similar diagnostic parameters.

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Automatic coronary blood flow computation: validation in quantitative flow ratio from coronary angiography

Cited by 22 publications

References 21 publications

Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law

Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law

Cardiovascular imaging 2019 in the International Journal of Cardiovascular Imaging

Meta-Analysis of Diagnostic Performance of Contrast-Fractional Flow Reserve versus Quantitative Flow Ratio for Functional Assessment of Coronary Stenoses

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