Journal Home > Volume 20 , Issue 1
BACKGROUND

Fractional flow reserve (FFR) is the invasive gold standard for evaluating coronary arterial stenosis. However, there have been a few non-invasive methods such as computational fluid dynamics FFR (CFD-FFR) with coronary CT angiography (CCTA) images that can perform FFR assessment. This study aims to develop a new method based on the principle of static first-pass of CT perfusion imaging technique (SF-FFR) and evaluate the efficacy in direct comparisons between CFD-FFR and the invasive FFR.

METHODS

A total of 91 patients (105 coronary artery vessels) who were admitted from January 2015 to March 2019 were enrolled in this study, retrospectively. All patients underwent CCTA and invasive FFR. 64 patients (75 coronary artery vessels) were successfully analyzed. The correlation and diagnostic performance of SF-FFR method on per-vessel basis were analyzed, using invasive FFR as the gold standard. As a comparison, we also evaluated the correlation and diagnostic performance of CFD-FFR.

RESULTS

The SF-FFR showed a good Pearson correlation (r = 0.70, P < 0.001) and intra-class correlation (r = 0.67, P < 0.001) with the gold standard. The Bland-Altman analysis showed that the average difference between the SF-FFR and invasive FFR was 0.03 (0.11–0.16); between CFD-FFR and invasive FFR was 0.04 (-0.10–0.19). Diagnostic accuracy and area under the ROC curve on a per-vessel level were 0.89, 0.94 for SF-FFR, and 0.87, 0.89 for CFD-FFR, respectively. The SF-FFR calculation time was about 2.5 s per case while CFD calculation was about 2 min on an Nvidia Tesla V100 graphic card.

CONCLUSIONS

The SF-FFR method is feasible and shows high correlation compared to the gold standard. This method could simplify the calculation procedure and save time compared to the CFD method.


menu
Abstract
Full text
Outline
About this article

Novel fast FFR derived from coronary CT angiography based on static first-pass algorithm: a comparison study

Show Author's information Lin YANG1Wen-Jia WANG2Chao XU1Tao BI1Yi-Ge LI2Si-Cong WANG2Lei XU1( )
Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
GE Healthcare China

Abstract

BACKGROUND

Fractional flow reserve (FFR) is the invasive gold standard for evaluating coronary arterial stenosis. However, there have been a few non-invasive methods such as computational fluid dynamics FFR (CFD-FFR) with coronary CT angiography (CCTA) images that can perform FFR assessment. This study aims to develop a new method based on the principle of static first-pass of CT perfusion imaging technique (SF-FFR) and evaluate the efficacy in direct comparisons between CFD-FFR and the invasive FFR.

METHODS

A total of 91 patients (105 coronary artery vessels) who were admitted from January 2015 to March 2019 were enrolled in this study, retrospectively. All patients underwent CCTA and invasive FFR. 64 patients (75 coronary artery vessels) were successfully analyzed. The correlation and diagnostic performance of SF-FFR method on per-vessel basis were analyzed, using invasive FFR as the gold standard. As a comparison, we also evaluated the correlation and diagnostic performance of CFD-FFR.

RESULTS

The SF-FFR showed a good Pearson correlation (r = 0.70, P < 0.001) and intra-class correlation (r = 0.67, P < 0.001) with the gold standard. The Bland-Altman analysis showed that the average difference between the SF-FFR and invasive FFR was 0.03 (0.11–0.16); between CFD-FFR and invasive FFR was 0.04 (-0.10–0.19). Diagnostic accuracy and area under the ROC curve on a per-vessel level were 0.89, 0.94 for SF-FFR, and 0.87, 0.89 for CFD-FFR, respectively. The SF-FFR calculation time was about 2.5 s per case while CFD calculation was about 2 min on an Nvidia Tesla V100 graphic card.

CONCLUSIONS

The SF-FFR method is feasible and shows high correlation compared to the gold standard. This method could simplify the calculation procedure and save time compared to the CFD method.

References(34)

[1]

Hu P, Tang MY, Song WC, et al. Fractional Flow Reserve Guided Percutaneous Coronary Intervention Improves Clinical Outcome with Reduced Cost in Contemporary Clinical Practice. Chin Med J (Engl) 2015; 128: 2000−2005.

[2]

Vanhoenacker PK, Heijenbrok-Kal MH, Van Heste R, et al. Diagnostic performance of multidetector CT angiography for assessment of coronary artery disease: meta-analysis. Radiology 2007; 244: 419−428.

[3]

Danad I, Szymonifka J, Twisk JWR, et al. Diagnostic performance of cardiac imaging methods to diagnose ischaemia-causing coronary artery disease when directly compared with fractional flow reserve as a reference standard: a meta-analysis. Eur Heart J 2017; 38: 991−998.

[4]

Min JK, Koo BK, Erglis A, et al. Usefulness of noninvasive fractional flow reserve computed from coronary computed tomographic angiograms for intermediate stenoses confirmed by quantitative coronary angiography. Am J Cardiol 2012; 110: 971−976.

[5]

Nørgaard BL, Leipsic J, Gaur S, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease. J Am Coll Cardiol 2014; 63: 1145−1155.

[6]

Koo BK, Erglis A, Doh JH, et al. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms: results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol 2011; 58: 1989−1997.

[7]

Lu MT, Ferencik M, Roberts RS, et al. Noninvasive FFR Derived From Coronary CT Angiography: Management and Outcomes in the PROMISE Trial. JACC Cardiovasc Imaging 2017; 10: 1350−1358.

[8]

Ko BS, Cameron JD, Munnur RK, et al. Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of Functionally Significant Stenosis. JACC Cardiovasc Imaging 2017; 10: 663−673.

[9]

Oishi A, Yagawa G. Computational mechanics enhanced by deep learning. Comput Methods Appl Mech Eng 2017; 327: 327−351.

[10]

Kishi S, Giannopoulos AA, Tang A, et al. Fractional Flow Reserve Estimated at Coronary CT Angiography in Intermediate Lesions: Comparison of Diagnostic Accuracy of Different Methods to Determine Coronary Flow Distribution. Radiology 2018; 287: 76−84.

[11]

Narula S, Shameer K, Salem Omar AM, et al. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol 2016; 68: 2287−2295.

[12]

Coenen A, Lubbers MM, Kurata A, et al. Coronary CT angiography derived fractional flow reserve: Methodology and evaluation of a point of care algorithm. J Cardiovasc Comput Tomogr 2016; 10: 105−113.

[13]

Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging 2018; 11: e007217.

[14]

Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol 2019; 16: 42−48.

[15]

Carson JM, Chakshu NK, Sazonov I, et al. Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve. Proc Inst Mech Eng H 2020; 234: 1337−1350.

[16]

Weigold WG, Abbara S, Achenbach S, et al. Standardized medical terminology for cardiac computed tomography: a report of the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr 2011; 5: 136−144.

[17]

Sun Z, Ng CK, Xu L, et al. Coronary CT angiography in heavily calcified coronary arteries: Improvement of coronary lumen visualization and coronary stenosis assessment with image postprocessing methods. Medicine 2015; 94: e2148.

[18]

Wang W, Yang L, Wang S, et al. An automated quantification method for the agatston coronary artery calcium score on coronary computed tomography angiography. Quant Imaging Med Surg 2022; 12: 1787−1799.

[19]

Seitun S, Castiglione Morelli M, Budaj I, et al. Stress Computed Tomography Myocardial Perfusion Imaging: A New Topic in Cardiology. Rev Esp Cardiol (Engl Ed) 2016; 69: 188−200.

[20]

Seitun S, De Lorenzi C, Cademartiri F, et al. CT myocardial perfusion imaging: a new frontier in cardiac imaging. Biomed Res Int 2018; 2018: 7295460.

[21]

Cademartiri F, Seitun S, Clemente A, et al. Myocardial blood flow quantification for evaluation of coronary artery disease by computed tomography. Cardiovasc Diagn Ther 2017; 7: 129−150.

[22]

Hanson EA, Sandmann C, Malyshev A, et al. Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements. PLoS One 2018; 13: e0200521.

[23]

Ishida M, Kitagawa K, Ichihara T, et al. Underestimation of myocardial blood flow by dynamic perfusion CT: explanations by two-compartment model analysis and limited temporal sampling of dynamic CT. J Cardiovasc Comput Tomogr 2016; 10: 207−214.

[24]

Mullani NA, Herbst RS, O’Neil RG, et al. Tumor blood flow measured by PET dynamic imaging of first-pass 18F-FDG uptake: a comparison with 15O-labeled water-measured blood flow. J Nucl Med 2008; 49: 517−523.

[25]

Kellner E, Gall P, Günther M, et al. Blood tracer kinetics in the arterial tree. PLoS One 2014; 9: e109230.

[26]

Carson JM, Pant S, Roobottom C, et al. Non-invasive coronary CT angiography-derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies. Int J Numer Method Biomed Eng 2019; 35: e3235.

[27]

Hindmarsh T. Elimination of water-soluble contrast media from the subarachnoid space. Investigation with computer tomography. Acta Radiol Suppl 1975; 346: 45−49.

[28]

Updegrove A, Wilson NM, Merkow J, et al. SimVascular: An Open Source Pipeline for Cardiovascular Simulation. Ann Biomed Eng 2017; 45: 525−541.

[29]

Min JK, Berman DS, Budoff MJ, et al. Rationale and design of the DeFACTO (Determination of Fractional Flow Reserve by Anatomic Computed Tomographic AngiOgraphy) study. J Cardiovasc Comput Tomogr 2011; 5: 301−309.

[30]

Lotfi A, Jeremias A, Fearon WF, et al. Expert consensus statement on the use of fractional flow reserve, intravascular ultrasound, and optical coherence tomography: a consensus statement of the Society of Cardiovascular Angiography and Interventions. Catheter Cardiovasc Interv 2014; 83: 509−518.

[31]

Hong C, Becker CR, Schoepf UJ, et al. Coronary artery calcium: absolute quantification in nonenhanced and contrast-enhanced multi-detector row CT studies. Radiology 2002; 223: 474−480.

[32]

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver-operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837−845.

[33]

Tesche C, De Cecco CN, Albrecht MH, et al. Coronary CT angiography-derived fractional flow reserve. Radiology 2017; 285: 17−33.

[34]

Li G, Wang H, Zhang M, et al. Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning. Commun Biol 2021; 4: 99.

Publication history
Copyright
Rights and permissions

Publication history

Published: 06 February 2023
Issue date: January 2023

Copyright

© 2023 JGC All rights reserved

Rights and permissions

Return