Skip to main content
SpringerLink
Log in

Comparison of machine learning–based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease

  • Cardiac
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To compare the diagnostic performance of machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis.

Methods

Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference.

Results

The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01).

Conclusion

ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis.

Clinical relevance statement

ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other.

Key Points

• Both machine learning (ML)–based computed tomography–derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis.

• Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis.

• ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AUC:

Area under curves

CAD:

Coronary artery disease

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

ICC:

Intraclass correlation coefficient

MBF:

Myocardial blood flow

ML:

Machine learning

MPR:

Myocardial perfusion reserve

MR:

Magnetic resonance

MVCAD:

Multivessel coronary artery disease (2- or 3-vessel obstructive disease)

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

References

  1. Greenwood JP, Maredia N, Younger JF et al (2012) Cardiovascular magnetic resonance and single-photo emission computed tomography for diagnosis of coronary heart disease (CE-MARC): a prospective trial. Lancet 379:453–600

    Article  PubMed  PubMed Central  Google Scholar 

  2. Danad I, Szymonifka J, Twisk JWR et al (2017) 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 38:991–998

    PubMed  Google Scholar 

  3. Arai AE, Schulz-Menger J, Berman D et al (2020) Gadobutrol-enhanced cardiac magnetic resonance imaging for detection of coronary artery disease. J Am Coll Cardiol 76(13):1536–1547

    Article  CAS  PubMed  Google Scholar 

  4. Heitner JF, Kim RJ, Kim HW et al (2019) Prognostic value of vasodilator stress cardiac magnetic resonance imaging: a multicenter study with 48000 patient-years of follow-up. JAMA Cardiol 4:256–264

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ge Y, Antiochos P, Steel K et al (2020) Prognostic value of stress CMR perfusion imaging in patients with reduced left ventricular function. JACC Cardiovasc Imaging 13:2132–2145

    Article  PubMed  PubMed Central  Google Scholar 

  6. Nagel E, Greenwood JP, McCann GP et al (2019) Magnetic resonance perfusion or fractional flow reserve in coronary disease. N Engl J Med 380:2418–2428

    Article  PubMed  Google Scholar 

  7. Schulz-Menger J, Bluemke DA, Bremerich J et al (2013) Standardized image interpretation and postprocessing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) Board of Trustees Task Force on Standardized Post Processing. J Cardiovasc Magn Reson 15:35

    Article  PubMed  PubMed Central  Google Scholar 

  8. Motwani M, Maredia N, Fairbairn TA, Kozerke S, Greenwood JP, Plein S (2014) Assessment of ischaemic burden in angiographic three-vessel coronary artery disease with high-resolution myocardial perfusion cardiovascular magnetic resonance imaging. Eur Heart J Cardiovasc Imaging 15:701–708

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kotecha T, Chacko L, Chehab O et al (2020) Assessment of multivessel coronary artery disease using cardiovascular magnetic resonance pixelwise quantitative perfusion mapping. JACC Cardiovasc Imaging 13:2546–2557

    Article  PubMed  Google Scholar 

  10. Hsu LY, Jacobs M, Benovoy M et al (2018) Diagnostic performance of fully automated pixel-wise quantitative myocardial perfusion imaging by cardiovascular magnetic resonance. JACC Cardiovasc Imaging 11:697–707

    Article  PubMed  PubMed Central  Google Scholar 

  11. Morton G, Chiribiri A, Ishida M et al (2012) Quantification of absolute myocardial perfusion in patients with coronary artery disease: comparison between cardiovascular magnetic resonance and positron emission tomography. J Am Coll Cardiol 60:1546–1555

    Article  PubMed  PubMed Central  Google Scholar 

  12. Rahman H, Scannell CM, Demir OM et al (2021) High-resolution cardiac magnetic resonance imaging techniques for the identification of coronary microvascular dysfunction. JACC Cardiovasc Imaging 14(5):978–986

    Article  PubMed  Google Scholar 

  13. Menke J, Kowalski J (2016) Diagnostic accuracy and utility of coronary CT angiography with consideration of unevaluable results: a systematic review and multivariate Bayesian random-effects meta-analysis with intention to diagnose. Eur Radiol 26:451–458

    Article  PubMed  Google Scholar 

  14. Meijboom WB, Van Mieghem CA, van Pelt N et al (2008) Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol 52:636–643

    Article  PubMed  Google Scholar 

  15. Koo BK, Erglis A, Doh JH et al (2011) 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 58(19):1989–1997

    Article  PubMed  Google Scholar 

  16. Nakazato R, Park HB, Berman DS et al (2013) Noninvasive fractional flow reserve derived from computed tomography angiography for coronary lesions of intermediate stenosis severity: results from the DeFACTO study. Circ Cardiovasc Imaging 6(6):881–889

    Article  PubMed  Google Scholar 

  17. Nørgaard BL, Leipsic J, Gaur S et al (2014) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 63(12):1145–1155

    Article  PubMed  Google Scholar 

  18. Douglas PS, Pontone G, Hlatky MA et al (2015) Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies versus usual care in patients with suspected coronary artery disease: the Prospective Longitudinal Trial of FFRCT: outcome and resource impacts (PLATFORM) study. Eur Heart J 36:3359–3367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Coenen A, Kim Y-H, Kruk M et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE Consortium. Circ Cardiovasc Imaging 11(6):e007217

    Article  PubMed  Google Scholar 

  20. Raff GL, Abidov A, Achenbach S et al (2009) SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography. J Cardiovasc Comput Tomogr 3:122–136

    Article  PubMed  Google Scholar 

  21. Itu L, Rapaka S, Passerini T et al (2016) A machine learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121(1):42–52

    Article  PubMed  Google Scholar 

  22. Ishida M, Schuster A, Morton G et al (2011) Development of a universal dual-bolus injection scheme for the quantitative assessment of myocardial perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson 13:28

    Article  PubMed  PubMed Central  Google Scholar 

  23. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 57:289–300

    MathSciNet  Google Scholar 

  24. Tesche C, De Cecco CN, Baumann S et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288(1):64–72

    Article  PubMed  Google Scholar 

  25. Rønnow Sand NP, Nissen L, Winther S et al (2020) Prediction of coronary revascularization in stable angina: comparison of FFRCT with CMR stress perfusion imaging. JACC Cardiovasc Imaging 13(4):994–1004

    Article  PubMed  Google Scholar 

  26. Sand NPR, Veien KT, Nielsen SS et al (2018) Prospective comparison of FFR derived from coronary CT angiography with SPECT perfusion imaging in stable coronary artery disease: the ReASSESS study. JACC Cardiovasc Imaging 11(11):1640–1650

    Article  PubMed  Google Scholar 

  27. Aznaouridis K, Masoura K, Tousoulis D (2018) Chapter 2.3 - regulation of oxygen transport and coronary blood flow. In: Tousoulis D (ed) Coronary artery disease. Academic Press; p 137–156

  28. Zhao SH, Guo WF, Yao ZF et al (2023) Fully automated pixel-wise quantitative CMR-myocardial perfusion with CMR-coronary angiography to detect hemodynamically significant coronary artery disease. Eur Radiol 33(10):7238–7249

    Article  CAS  PubMed  Google Scholar 

  29. Maron DJ, Hochman JS, Reynolds HR et al (2020) Initial invasive or conservative strategy for stable coronary disease. N Engl J Med 382:1395–1407

    Article  PubMed  PubMed Central  Google Scholar 

  30. SCOT-HEART Investigators, Newby DE, Adamson PD et al (2018) Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med 379:924–933

    Article  Google Scholar 

  31. Hoffmann U, Ferencik M, Udelson JE et al (2017) Prognostic value of noninvasive cardiovascular testing in patients with stable chest pain: insights from the PROMISE trial (Prospective Multicenter Imaging Study for Evaluation of Chest Pain). Circulation 135:2320–2332

    Article  PubMed  PubMed Central  Google Scholar 

  32. Foy AJ, Dhruva SS, Peterson B, Mandrola JM, Morgan DJ, Redberg RF (2017) Coronary computed tomography angiography versus functional stress testing for patients with suspected coronary artery disease: a systematic review and meta-analysis. JAMA Intern Med 177:1623–1631

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge Dr. Schwemmer Chris, Siemens Healthineers, Forchheim, Germany, for providing software of machine learning CT fractional flow reserve; Dr. Yihan Lu and Xue Yang, Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, for contributing to interpretation of coronary CTA; Dr. Yang Qiao, Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, for contributing to analysis of machine learning CT fractional flow reserve.

Funding

This study has received funding by Shanghai Municipal Key Clinical Specialty (grant number shslczdzk03202).

Author information

Author notes

  1. Weifeng Guo, Shihai Zhao, and Haijia Xu as co-first authors contributed equally to this work.

  2. Mengsu Zeng, Chenguang Li, and Shan Yang as co-corresponding authors contributed equally to this study.

Authors and Affiliations

  1. Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China

    Weifeng Guo, Shihai Zhao, Lekang Yin, Hang Jin, Dong Wu, Shan Yang & Mengsu Zeng

  2. Department of Radiology, Shanghai Geriatric Medical Center, 2560 Chunshen Road, Minhang District, Shanghai, 201104, China

    Weifeng Guo, Shihai Zhao, Lekang Yin, Hang Jin, Dong Wu & Mengsu Zeng

  3. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Haijia Xu, Zhifeng Yao & Chenguang Li

  4. School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China

    Haijia Xu

  5. Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Wei He

  6. Siemens Healthineers China, Shanghai, China

    Zhihan Xu

Authors
  1. Weifeng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shihai Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haijia Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lekang Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhifeng Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhihan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hang Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chenguang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mengsu Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mengsu Zeng.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Mengsu Zeng.

Conflict of Interest

Z.X. is an employee of Siemens. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and Biometry

No complex statistical methods were necessary for this paper.

Informed Consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical Approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

None.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 336 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, W., Zhao, S., Xu, H. et al. Comparison of machine learning–based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10650-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00330-024-10650-6

Keywords

Search

Navigation