Skip to main content
SpringerLink
Log in

Integrated myocardial flow reserve (iMFR) assessment: optimized PET blood flow quantification for diagnosis of coronary artery disease

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Distinguishing obstructive epicardial coronary artery disease (CAD) from microvascular dysfunction and diffuse atherosclerosis would be of immense benefit clinically. However, quantitative measures of absolute myocardial blood flow (MBF) integrate the effects of focal epicardial stenosis, diffuse atherosclerosis, and microvascular dysfunction. In this study, MFR and relative perfusion quantification were combined to create integrated MFR (iMFR) which was evaluated using data from a large clinical registry and an international multi-center trial and validated against invasive coronary angiography (ICA).

Methods

This study included 1,044 clinical patients referred for 82Rb rest/stress positron emission tomography myocardial perfusion imaging and ICA, along with 231 patients from the Flurpiridaz 301 trial (clinicaltrials.gov NCT01347710). MFR and relative perfusion quantification were combined to create an iMFR map. The incremental value of iMFR was evaluated for diagnosis of obstructive stenosis, adjusted for patient demographics and pre-test probability of CAD. Models for high-risk anatomy (left main or three-vessel disease) were also constructed.

Results

iMFR parameters of focally impaired perfusion resulted in best fitting diagnostic models. Receiver-operating characteristic analysis showed a slight improvement compared to standard quantitative perfusion approaches (AUC 0.824 vs. 0.809). Focally impaired perfusion was also associated with high-risk CAD anatomy (OR 1.40 for extent, and OR 2.40 for decreasing mean MFR). Diffusely impaired perfusion was associated with lower likelihood of obstructive CAD, and, in the absence of transient ischemic dilation (TID), with lower likelihood of high-risk CAD anatomy.

Conclusions

Focally impaired perfusion extent derived from iMFR assessment is a powerful incremental predictor of obstructive CAD while diffusely impaired perfusion extent can help rule out obstructive and high-risk CAD in the absence of TID.

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

Data availability

Individual subject level data underlying this article cannot be shared publicly due to medical data privacy regulations. The data may be shared on reasonable request under data use agreements with the University of Michigan and INVIA.

References

  1. Ford TJ, Stanley B, Good R, et al. Stratified Medical Therapy Using Invasive Coronary Function Testing in Angina: The CorMicA Trial. J Am Coll Cardiol. 2018;72:2841–55. https://doi.org/10.1016/J.JACC.2018.09.006.

    Article  PubMed  Google Scholar 

  2. Murthy VL, Bateman TM, Beanlands RS, et al. Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC. J Nucl Med. 2018;59:273–93. https://doi.org/10.2967/jnumed.117.201368.

    Article  CAS  PubMed  Google Scholar 

  3. Gupta A, Taqueti VR, van de Hoef TP, et al. Integrated Noninvasive Physiological Assessment of Coronary Circulatory Function and Impact on Cardiovascular Mortality in Patients With Stable Coronary Artery Disease. Circulation. 2017;136:2325–36. https://doi.org/10.1161/CIRCULATIONAHA.117.029992.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ziadi MC, DeKemp RA, Williams K, et al. Does quantification of myocardial flow reserve using rubidium-82 positron emission tomography facilitate detection of multivessel coronary artery disease? J Nucl Cardiol. 2012;19:670–80. https://doi.org/10.1007/S12350-011-9506-5/FIGURES/5.

    Article  PubMed  Google Scholar 

  5. Zampella E, Acampa W, Assante R, et al. Combined evaluation of regional coronary artery calcium and myocardial perfusion by 82Rb PET/CT in the identification of obstructive coronary artery disease. Eur J Nucl Med Mol Imaging. 2018;45:521–9. https://doi.org/10.1007/s00259-018-3935-1.

    Article  CAS  PubMed  Google Scholar 

  6. Johnson NP, Gould KL. Integrating noninvasive absolute flow, coronary flow reserve, and ischemic thresholds into a comprehensive map of physiological severity. JACC Cardiovasc Imaging. 2012;5:430–40. https://doi.org/10.1016/J.JCMG.2011.12.014.

    Article  PubMed  Google Scholar 

  7. Gould KL, Johnson NP, Roby AE, et al. Regional, Artery-Specific Thresholds of Quantitative Myocardial Perfusion by PET Associated with Reduced Myocardial Infarction and Death After Revascularization in Stable Coronary Artery Disease. J Nucl Med. 2019;60:410–7. https://doi.org/10.2967/jnumed.118.211953.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gould KL, Kitkungvan D, Johnson NP, et al. Mortality Prediction by Quantitative PET Perfusion Expressed as Coronary Flow Capacity With and Without Revascularization. JACC Cardiovasc Imaging. 2021;14:1020–34. https://doi.org/10.1016/j.jcmg.2020.08.040.

    Article  PubMed  Google Scholar 

  9. Arida-Moody L, Moody JB, Renaud JM, et al (2021) Effects of two patient-specific dosing protocols on measurement of myocardial blood flow with 3D 82Rb cardiac PET. Eur J Nucl Med Mol Imaging 1–12. https://doi.org/10.1007/s00259-021-05385-1.

  10. A Phase 3 Multi-center Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD. - Full Text View - ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT01347710. Accessed 15 Jun 2021.

  11. Maddahi J, Lazewatsky J, Udelson JE, et al. Phase-III Clinical Trial of Fluorine-18 Flurpiridaz Positron Emission Tomography for Evaluation of Coronary Artery Disease. J Am Coll Cardiol. 2020;76:391–401.

    Article  CAS  PubMed  Google Scholar 

  12. Moody JB, Poitrasson-Rivière A, Hagio T, et al. Added value of myocardial blood flow using 18F-flurpiridaz PET to diagnose coronary artery disease: The flurpiridaz 301 trial. J Nucl Cardiol. 2020. https://doi.org/10.1007/s12350-020-02034-2.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ficaro EP, Lee BC, Kritzman JN, Corbett JR. Corridor4DM: The Michigan method for quantitative nuclear cardiology. J Nucl Cardiol. 2007;14:455–65. https://doi.org/10.1016/j.nuclcard.2007.06.006.

    Article  PubMed  Google Scholar 

  14. Poitrasson-Rivière A, Moody JB, Renaud JM, et al. Impact of residual subtraction on myocardial blood flow and reserve estimates from rapid dynamic PET protocols. J Nucl Cardiol. 2021;2021:1–9. https://doi.org/10.1007/S12350-021-02837-X.

    Article  Google Scholar 

  15. Lortie M, Beanlands RSB, Yoshinaga K, et al. Quantification of myocardial blood flow with 82Rb dynamic PET imaging. Eur J Nucl Med Mol Imaging. 2007;34:1765–74. https://doi.org/10.1007/s00259-007-0478-2.

    Article  PubMed  Google Scholar 

  16. Nekolla SG, Reder S, Saraste A, et al. Evaluation of the novel myocardial perfusion positron-emission tomography tracer 18F-BMS-747158-02: Comparison to 13N-ammonia and validation with microspheres in a pig model. Circulation. 2009;119:2333–42. https://doi.org/10.1161/CIRCULATIONAHA.108.797761.

    Article  CAS  PubMed  Google Scholar 

  17. Slomka PJ, Nishina H, Berman DS, et al. Automated quantification of myocardial perfusion SPECT using simplified normal limits. J Nucl Cardiol. 2005;12:66–77. https://doi.org/10.1016/j.nuclcard.2004.10.006.

    Article  PubMed  Google Scholar 

  18. Naya M, Murthy VL, Taqueti VR, et al. Preserved Coronary Flow Reserve Effectively Excludes High-Risk Coronary Artery Disease on Angiography. J Nucl Med. 2014;55:248–55. https://doi.org/10.2967/jnumed.113.121442.

    Article  PubMed  Google Scholar 

  19. Genders TSS, Coles A, Hoffmann U, et al. The External Validity of Prediction Models for the Diagnosis of Obstructive Coronary Artery Disease in Patients With Stable Chest Pain. JACC Cardiovasc Imaging. 2018;11:437–46. https://doi.org/10.1016/j.jcmg.2017.02.020.

    Article  PubMed  Google Scholar 

  20. Bozdogan H. Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychom. 1987;52(3):345–70. https://doi.org/10.1007/BF02294361.

    Article  Google Scholar 

  21. Sakamoto Y, Ishiguro M, Kitagawa G. Akaike information criterion statistics. Springer Dordrecht; 1986

  22. 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. https://doi.org/10.2307/2531595.

    Article  CAS  PubMed  Google Scholar 

  23. Moody JB, Poitrasson-Rivière A, Renaud JM, et al. Integrated myocardial flow reserve (iMFR) assessment: diffuse atherosclerosis and microvascular dysfunction are more strongly associated with mortality than focally impaired perfusion. Eur J Nucl Med Mol Imaging Res. 2023;In press. https://doi.org/10.1007/s00259-023-06448-1

  24. Yoshinaga K, Katoh C, Manabe O, et al. Incremental Diagnostic Value of Regional Myocardial Blood Flow Quantification Over Relative Perfusion Imaging With Generator-Produced Rubidium-82 PET. Circ J. 2011;75:2628–34. https://doi.org/10.1253/CIRCJ.CJ-11-0502.

    Article  PubMed  Google Scholar 

  25. Parkash R, deKemp RA, Ruddy TD, et al. (2004) Potential utility of rubidium 82 pet quantification in patients with 3-vessel coronary artery disease. J Nucl Cardiol. 2004;114(11):440–9. https://doi.org/10.1016/J.NUCLCARD.2004.04.005.

    Article  Google Scholar 

  26. Poitrasson-Rivière A, Moody JB, Hagio T, et al. Reducing motion-correction-induced variability in 82rubidium myocardial blood-flow quantification. J Nucl Cardiol. 2020;27:1104–13. https://doi.org/10.1007/s12350-019-01911-9

Download references

Acknowledgements

The authors thank R.A. deKemp (Ottawa Heart Institute) for providing CFC thresholds appropriate for the 1-tissue-compartment kinetic model. The authors acknowledge the Regents of the University of Michigan for the use of de-identified clinical data for this study. The authors acknowledge GE for the use of de-identified data from the Flurpiridaz 301 trial (NCT01347710) data for this study.

Funding

VLM is supported by the Melvyn Rubenfire Professorship in Preventive Cardiology.

Author information

Authors and Affiliations

  1. INVIA Medical Imaging Solutions, 3025 Boardwalk Dr., Suite 200, Ann Arbor, MI, 48108, USA

    Alexis Poitrasson-Rivière, Jonathan B. Moody, Jennifer M. Renaud, Tomoe Hagio & Edward P. Ficaro

  2. Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Liliana Arida-Moody, Brahmajee K. Nallamothu & Venkatesh L. Murthy

  3. GE Pharmaceutical Diagnostics R&D, Amersham, UK

    Christopher J Buckley

  4. Houston Methodist Debakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX, USA

    Mouaz H. Al-Mallah

  5. Division of Cardiology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Richard L. Weinberg

Authors
  1. Alexis Poitrasson-Rivière
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan B. Moody
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jennifer M. Renaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tomoe Hagio
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liliana Arida-Moody
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christopher J Buckley
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mouaz H. Al-Mallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Brahmajee K. Nallamothu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Richard L. Weinberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Edward P. Ficaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Venkatesh L. Murthy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexis Poitrasson-Rivière.

Ethics declarations

Competing interests

APR, JBM, JMR, and TH are employees of INVIA. JMR is a consultant for Jubilant Radiopharma and receives royalties from licensing of the FlowQuant software. EPF is a stockholder in INVIA. VLM has received research grants and speaking honoraria from Siemens Medical Imaging and serves as a scientific advisor for Ionetix and owns stock options in the same. He owns stock in GE and Cardinal Health, has received expert witness payments on behalf of Jubilant Radiopharma and a speaking honorarium from 2Quart Medical, and receives consulting payments and research support from INVIA. He is also supported by grants R01AG059729 from the National Institute on Aging, U01DK123013 from the National Institute of Diabetes and Digestive and Kidney Disease, and R01HL136685 from the National Heart, Lung, and Blood Institute.

Additional information

Publisher's Note

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

Edward P. Ficaro and Venkatesh L. Murthy share equal contributions as co-senior authors.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 65.9 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

Poitrasson-Rivière, A., Moody, J.B., Renaud, J.M. et al. Integrated myocardial flow reserve (iMFR) assessment: optimized PET blood flow quantification for diagnosis of coronary artery disease. Eur J Nucl Med Mol Imaging 51, 136–146 (2023). https://doi.org/10.1007/s00259-023-06455-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-023-06455-2

Keywords

Search

Navigation