Skip to main content

Advertisement

SpringerLink
Log in

Clinical implications of compromised 82Rb PET data acquisition

  • Original Article
  • Published:
Journal of Nuclear Cardiology Aims and scope

Abstract

Background

We wished to document the prevalence and quantitative effects of compromised 82Rb PET data acquisitions on myocardial flow reserve (MFR).

Methods and Results

Data were analyzed retrospectively for 246 rest and regadenoson-stress studies of 123 patients evaluated for known or suspected CAD. An automated injector delivered pre-determined activities of 82Rb. Automated quality assurance algorithms identified technical problems for 7% (9/123) of patients. Stress data exhibited 2 instances of scanner saturation, 1 blood peak detection, 1 blood peak width, 1 gradual patient motion, and 2 abrupt patient motion problems. Rest data showed 1 instance of blood peak width and 2 abrupt patient motion problems. MFR was lower for patients with technical problems flagged by the quality assurance algorithms than those without technical problems (1.5 ± 0.5 versus 2.1 ± 0.7, P = 0.01), even though rest and stress ejection fraction, asynchrony and relative myocardial perfusion measures were similar for these two groups (P > 0.05), suggesting that MFR accuracy was adversely affected by technical errors.

Conclusion

It is important to verify integrity of 82Rb data to ensure MFR computation quality.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

Abbreviations

ECTb:

Emory Cardiac Toolbox software

EF:

Ejection fraction

LV:

Left ventricle

MBF:

Myocardial blood flow

MFR:

Myocardial flow reserve

PET:

Positron emission tomography

QA:

Quality assurance

ROI:

Region of interest

RV:

Right ventricle

References

  1. Cullom SJ, Case JA, Courter SA, McGhie AI, Bateman TM. Regadenoson pharmacologic rubidium-82 PET: a comparison of quantitative perfusion and function to dipyridamole. J Nucl Cardiol 2013;20:76-83.

    Article  Google Scholar 

  2. Hagemann CE, Ghotbi AA, Kjær A, Hasbak P. Quantitative myocardial blood flow with Rubidium-82 PET: a clinical perspective. Am J Nucl Med Mol Imaging 2015;5:457-68.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Murthy VL, Bateman TM, Beanlands RS, Berman DS, Borges-Netok S, Chareonthaitawee P, et al. Clinical quantification of myocardial blood flow using PET: Joint position paper of the SNMMI Cardiovascular Council and the ASNC. J Nucl Cardiol 2018;25:269-97.

    Article  Google Scholar 

  4. Renaud JM, Yip K, Guimond J, Trottier M, Pibarot P, Turcotte E, et al. Characterization of 3-dimensional PET systems for accurate quantification of myocardial blood flow. J Nucl Med 2017;58:103-9.

    Article  CAS  Google Scholar 

  5. Rajaram M, Tahari AK, Lee AH, Lodge MA, Tsui B, Nekolla S, et al. Cardiac PET/CT misregistration causes significant changes in estimated myocardial blood flow. J Nucl Med 2013;54:50-4.

    Article  Google Scholar 

  6. Hunter CRRN, Klein R, Beanlands RS, deKemp RA. Patient motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging. Med Phys 2016;43:1829.

    Article  Google Scholar 

  7. Van Tosh A, Votaw JR, Cooke CD, Reichek N, Palestro CJ, Nichols KJ. Relationships between left ventricular asynchrony and myocardial blood flow. J Nucl Cardiol 2017;24:43-52.

    Article  Google Scholar 

  8. Bravo PE, Pozios I, Pinheiro A, Merrill J, Tsui BMW, Wahl RL, et al. Comparison and effectiveness of regadenoson versus dipyridamole on stress electrocardiographic changes during positron emission tomography evaluation of patients with hypertrophic cardiomyopathy. Am J Cardiol 2012;110:1033-9.

    Article  CAS  Google Scholar 

  9. Johnson NP, Gould KL. Regadenoson versus dipyridamole hyperemia for cardiac PET imaging. JACC Cardiovasc Imaging 2015;8:438-47.

    Article  Google Scholar 

  10. Votaw JR, Packard RRS. Technical aspects of acquiring and measuring myocardial blood flow: Method, technique, and QA. J Nucl Cardiol 2017. https://doi.org/10.1007/s12350-017-1049-y.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Garcia EV, Klein JL, Moncayo V, Cooke CD, Del’Aune C, Folks R, et al. Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging. J Nucl Cardiol 2018. https://doi.org/10.1007/s12350-018-1432-3.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Faber TL, Cooke CD, Folks RD, Vansant JP, Nichols KJ, DePuey EG, et al. Left ventricular function from gated SPECT perfusion images: An integrated method. J Nucl Med 1999;40:650-9.

    CAS  PubMed  Google Scholar 

  13. Chen J, Garcia EV, Folks RD, Cooke CD, Faber TL, Tauxe EL, et al. Onset of left ventricular mechanical contraction as determined by phase analysis of ECG-gated myocardial perfusion SPECT imaging: Development of a diagnostic tool for assessment of cardiac mechanical dyssynchrony. J Nucl Cardiol 2005;12:68-95.

    Article  Google Scholar 

  14. Renkin EM. Transport of potassium-42 from blood to tissue isolated mammalian skeletal muscles. Am J Physiol 1959;197:1205-10.

    Article  CAS  Google Scholar 

  15. Crone C. Permeability of capillaries in various organs as determined by use of the indicator diffusion method. Acta Physiol Scand 1963;58:292-305.

    Article  CAS  Google Scholar 

  16. Ficaro EP, Lee BC, Kritzman JN, Corbett JR. Corridor4DM: The Michigan method for quantitative nuclear cardiology. J Nucl Cardiol 2007;14:455-65.

    Article  Google Scholar 

  17. Konerman MC, Lazarus JJ, Weinberg RL, Shah RV, Ghannam M, Hummel SL, et al. Reduced myocardial flow reserve by positron emission tomography predicts cardiovascular events after cardiac transplantation. Circ Heart Fail 2018;11:e004473. https://doi.org/10.1161/CIRCHEARTFAILURE.117.004473.

    Article  PubMed  PubMed Central  Google Scholar 

  18. El Fakhri G, Kardan A, Sitek A, Dorbala S, Abi-Hatem N, Lahoud Y, et al. Reproducibility and accuracy of quantitative myocardial blood flow assessment with (82)Rb PET: Comparison with (13)N-ammonia PET. J Nucl Med 2009;50:1062-71. https://doi.org/10.2967/jnumed.104.007831.

    Article  CAS  PubMed  Google Scholar 

  19. Yoshida K, Mullani N, Gould KL. Coronary flow and flow reserve by PET simplified for clinical applications using rubidium-82 or nitrogen-13-ammonia. J Nucl Med 1996;37:1701-12.

    CAS  PubMed  Google Scholar 

  20. MedCalc® Statistical Software version 19.6 (MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org; 2020)

  21. Taqueti VR, Hachamovitch R, Murthy VL, Naya M, Foster CR, Hainer J, et al. Global coronary flow reserve is associated with adverse cardiovascular events independently of luminal angiographic severity and modifies the effect of early revascularization. Circulation 2015;131:19-27.

    Article  Google Scholar 

  22. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-74.

    Article  CAS  Google Scholar 

  23. Raylman RR, Caraher JM, Hutchins GD. Sampling requirements for dynamic cardiac PET studies using image-derived input functions. J Nucl Med 1993;34:440-7.

    CAS  PubMed  Google Scholar 

  24. Klein R, Ocneanu A, Renaud JM, Ziadi MC, Beanlands RSB, deKemp RA. Consistent tracer administration profile improves test-retest repeatability of myocardial blood flow quantification with 82Rb dynamic PET imaging. J Nucl Cardiol 2018;25:929-41.

    Article  Google Scholar 

  25. Moody JB, Lee BC. Precision and accuracy of clinical quantification of myocardial blood flow by dynamic PET: A technical perspective. J Nucl Cardiol 2015;22:935-51.

    Article  Google Scholar 

  26. Thompson AD, Ficaro EP, Murthy VL, Weinberg RL. Rescued diagnostic quality by motion correction of dynamic cardiac positron emission tomography (PET) perfusion images. J Nucl Cardiol 2019;26:330-2.

    Article  Google Scholar 

  27. Lee BC, Moody JB, Poitrasson-Riviere A, Melvin AC, Weinberg RL, Corbett JR, et al. Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow quantification. J Nucl Cardiol 2020;27:1982-98.

    Article  Google Scholar 

  28. Sorrell V, Figueroa B, Hansen CL. The “hurricane sign”: evidence of patient motion artifact on cardiac single-photon emission computed tomographic imaging. J Nucl Cardiol 1996;3:86-8.

    Article  CAS  Google Scholar 

  29. Lee BC, Moody JB, Poitrasson-Riviere A, Melvin AC, Weinberg RL, Corbett JR, et al. Blood pool and tissue phase patient motion effects on 82rubidium PET myocardial blood flow quantification. J Nucl Cardiol 2019;26:1918-29.

    Article  Google Scholar 

  30. Otaki Y, Lassen ML, Manabe O, Eisenberg E, Gransar H, Wang F, et al. Short-term repeatability of myocardial blood flow using 82Rb PET/CT: the effect of arterial input function position and motion correction. J Nucl Cardiol 2019. https://doi.org/10.1007/s12350-019-01888-5.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Memmott MJ, Tonge CM, Saint KJ, Arumugam P. Impact of pharmacological stress agent on patient motion during rubidium-82 myocardial perfusion PET/CT. J Nucl Cardiol 2018;25:1286-95.

    Article  Google Scholar 

  32. van Dijk JD, van Dalen JA, Mouden M, Ottervanger JP, Knollema S, Slump CH, et al. Value of automatic patient motion detection and correction in myocardial perfusion imaging using a CZT-based SPECT camera. J Nucl Cardiol 2018;25:419-28.

    Article  Google Scholar 

  33. Hajjiri MM, Leavitt MB, Zheng H, Spooner AE, Fischman AJ, Gewirtz H. Comparison of positron emission tomography measurement of adenosine-stimulated absolute myocardial blood flow versus relative myocardial tracer content for physiological assessment of coronary artery stenosis severity and location. JACC Cardiovasc Imaging 2009;2:751-8.

    Article  Google Scholar 

  34. Ziadi MC, deKemp RA, Williams KA, Guo A, Chow BJ, Renaud JM, et al. Impaired myocardial flow reserve on rubidium-82 positron emission tomography imaging predicts adverse outcomes in patients assessed for myocardial ischemia. J Am Coll Cardiol 2011;58:740-8.

    Article  Google Scholar 

  35. Dunet V, Klein R, Allenbach G, Renaud J, deKemp RA, Prior JO. Myocardial blood flow quantification by Rb-82 cardiac PET/CT: A detailed reproducibility study between two semi-automatic analysis programs. J Nucl Cardiol 2016;23:499-510.

    Article  Google Scholar 

  36. Murthy VL, Lee BC, Sitek A, Naya M, Moody J, Polavarapu V, et al. Comparison and prognostic validation of multiple methods of quantification of myocardial blood flow with 82Rb PET. J Nucl Med 2014;55:1952-8.

    Article  CAS  Google Scholar 

  37. Hunter CRRN, Klein R, Alessio AM, deKemp RA. Patient body motion correction for dynamic cardiac PET-CT by attenuation-emission alignment according to projection consistency conditions. Med Phys 2019;46:1697-706.

    Article  Google Scholar 

  38. Chamberland MJP, deKemp RA, Xu T. Motion tracking of low-activity fiducial markers using adaptive region of interest with list-mode positron emission tomography. Med Phys 2020;47:3402-14.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. St. Francis Hospital, Roslyn, NY, USA

    Andrew Van Tosh MD & J. Jane Cao MD, MPH

  2. Emory University, Atlanta, GA, USA

    John R. Votaw PhD & C. David Cooke MSEE

  3. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA

    Christopher J. Palestro MD & Kenneth J. Nichols PhD

  4. Research Department, St. Francis Hospital, 100 Port Washington Blvd., Roslyn, NY, 11576-1348, USA

    Andrew Van Tosh MD

Authors
  1. Andrew Van Tosh MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Jane Cao MD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John R. Votaw PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. David Cooke MSEE
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christopher J. Palestro MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kenneth J. Nichols PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kenneth J. Nichols PhD.

Ethics declarations

Disclosures

Andrew Van Tosh serves as a consultant to Astellas Pharma Global Development. Inc. John R Votaw, C David Cooke, and Kenneth Nichols participate in royalties, Syntermed, Inc.

Additional information

Publisher's Note

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

The authors of this article have provided a PowerPoint file, available for download at SpringerLink, which summarizes the contents of the paper and is free for re-use at meetings and presentations. Search for the article DOI on SpringerLink.com.

The authors have also provided an audio summary of the article, which is available to download as ESM, or to listen to via the JNC/ASNC Podcast.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Van Tosh, A., Cao, J.J., Votaw, J.R. et al. Clinical implications of compromised 82Rb PET data acquisition. J. Nucl. Cardiol. 29, 2583–2594 (2022). https://doi.org/10.1007/s12350-021-02774-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12350-021-02774-9

Keywords

Search

Navigation