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

  • Yimin Zhang
  • Su Zhang
  • Jelmer Westra
  • Daixin Ding
  • Qiuyang Zhao
  • Junqing Yang
  • Zhongwei Sun
  • Jiayue Huang
  • Jun Pu
  • Bo Xu
  • Shengxian TuEmail author
Original Paper


To assess a novel approach for automatic flow velocity computation in deriving quantitative flow ratio (QFR) from coronary angiography. QFR is a novel approach for assessment of functional significance of coronary artery stenosis without using pressure wire and induced hyperemia. Patient-specific coronary flow is estimated semi-automatically by frame count method, which is subjective and inconvenient in the workflow of QFR analysis. The vascular structures were automatically delineated from coronary angiogram. Subsequently, the centerline of the interrogated vessel was extracted from the delineated lumen on each image frame and the change in the length of centerline was used to compute the flow velocity, which provided patient-specific flow for computation of QFR (QFRauto). A parameter derived from the increase in centerline length was used to automatically quantify the stability of contrast flow. From the two angiographic image runs used for three-dimensional angiographic reconstruction, the one with better stability was used to compute QFRauto. QFRauto was assessed in all patients enrolled in the FAVOR II China study, and compared with the commercialized QFR computational method based on frame count (QFRcount), using pressure wire-based fractional flow reserve (FFR) as the reference standard. Out of 328 vessels with paired FFR data, QFRauto was successfully computed on 325 (99%) vessels with acceptable stability in filling of contrast flow. The flow velocity computed by the proposed approach had a weak to moderate correlation with the frame count method (r = 0.37, p < 0.001), with mean differences of − 0.02 ± 0.07 m/s (p < 0.001). QFRauto had good correlation (r = 0.96, p < 0.001) and agreement (mean difference: − 0.01 ± 0.04, p < 0.001) with QFRcount. Good correlation (r = 0.83, p < 0.001) and agreement (mean difference: 0.01 ± 0.06, p = 0.016) were also observed between QFRauto and FFR. Using FFR ≤ 0.80 to define functional significance of coronary stenosis, the overall diagnostic accuracy for QFRauto was 93.2% (95% CI 90.5–96.0%). The area under the receiver-operating characteristic curve did not differ significantly between QFRcount and QFRauto (difference: 0.00; 95% CI − 0.01 to 0.01; p = 0.529). Sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio for QFRauto were 92.4% (95% CI 86.0–96.5%), 93.7% (95% CI 89.5–96.6%), 14.7 (95% CI 8.7–25.0), and 0.1 (95% CI 0.0–0.2), respectively. Automatic computation of patient-specific coronary flow velocity based on coronary angiography is feasible. Assessment of QFR based on this novel approach had good diagnostic accuracy in determining the functional significance of coronary stenosis.


Fractional flow reserve Quantitative flow ratio Coronary blood flow Coronary angiography Image processing 



Areas under the receiver-operator characteristics curve


Confidence interval


Fractional flow reserve


Left anterior descending


Quantitative flow velocity


QFR computed by Vauto


QFR computed by Vcount


Right coronary artery


Automatically calculated flow velocity


Frame count-based flow velocity



This study was supported by the National Key Research and Development Program of China (Grant No. 2016YFC0100500), the Natural Science Foundation of China (Grant No. 81871460 and 31500797), Shanghai ShenKang Hospital Development Center (16CR3034A), and Shanghai Jiao Tong University (Grant No. YG2015ZD04 and YG2016ZD09).

Compliance with ethical standards

Conflict of interest

None of the Authors have declared a conflict of interest in relation to this study, with the exception that S Tu received research support from Medis medical imaging and Pulse medical imaging. Other authors report no conflicts of interest regarding this manuscript.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Biomedical Instrument Institute, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Med-X Engineering Research CenterShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Department of CardiologyAarhus University HospitalSkejbyDenmark
  4. 4.Guangdong General HospitalGuangzhouChina
  5. 5.Fu Wai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical SciencesBeijingChina
  6. 6.Renji HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

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