Skip to main content
Log in

Effect of iterations and time of flight on normal distributions of 82Rb PET relative perfusion and myocardial blood flow

Journal of Nuclear Cardiology Aims and scope

Abstract

Background

As clinical use of myocardial blood flow (MBF) increases, dynamic series are becoming part of the typical workflow. The methods and parameters used to reconstruct these series require investigation to ensure accurate quantification.

Methods

Fifty-nine rest/stress dynamic 82Rb PET studies, acquired on a Biograph mCT, from a combination of normal volunteers and low-likelihood patients were reconstructed with and without time of flight (TOF) for varying iterations and processed to obtain relative perfusion and MBF polar maps. Regional values from mean polar maps were fit to a linear mixed-effect model to quantify convergence and select the optimal number of iterations.

Results

TOF reconstructions converged faster and yielded more uniform relative perfusion polar maps. However, the stress MBF distribution for TOF reconstructions was more heterogeneous, with a higher-intensity septal wall. This phenomenon requires further investigation, with right ventricle blood pool spillover possibly having an effect. Optimal reconstructions were defined as 5-iteration non-TOF (24-subset) reconstructions and 3-iteration TOF (21-subset) reconstructions.

Conclusion

Optimal cardiac reconstructions were identified for non-TOF and TOF reconstructions of dynamic series. TOF reconstruction presents as the more accurate method, given the more uniform relative perfusion distribution.

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

Access this article

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
Figure 7
Figure 8

Abbreviations

PET:

Positron emission tomography

CT:

Computed tomography

TOF:

Time of flight

Rb:

Rubidium

MBF:

Myocardial blood flow

MFR:

Myocardial flow reserve

LV:

Left ventricle

RV:

Right ventricle

BP:

Blood pool

References

  1. Jaskowiak CJ, Bianco JA, Perlman SB, Fine JP. Influence of reconstruction iterations on 18F-FDG PET/CT standardized uptake values. J Nucl Med 2005;46:424-8.

    PubMed  Google Scholar 

  2. Yamanoi R, Okamoto K, Kawamura K, Ohkubo M, Suzuki Y, Nakamura Y. Pixel values of [15O]H2O2 PET images with OSEM algorithm depending on numbers of subset and iteration times: comparative assessment to FBP. Kaku Igaku 2014;51:373-82.

    CAS  PubMed  Google Scholar 

  3. Liow JS, Strother SC. Practical tradeoffs between noise, quantitation, and number of iterations for maximum likelihood-based reconstructions. IEEE Trans. Med. Imaging 1991;10:563-71.

    Article  CAS  Google Scholar 

  4. Liow JS, Strother SC. The convergence of object dependent resolution in maximum likelihood based tomographic image reconstruction. Phys Med Biol 1993;38:55-70.

    Article  CAS  Google Scholar 

  5. Dasari PKR, Jones JP, Casey ME, Liang Y, Dilsizian V, Smith MF. The effect of time-of-flight and point spread function modeling on 82Rb myocardial perfusion imaging of obese patients. J Nucl Cardiol 2018;25:1521-45.

    Article  Google Scholar 

  6. Alessio AM, Stearns CW, Tong S, et al. Application and evaluation of a measured spatially variant system model for PET image reconstruction. IEEE Trans Med Imaging 2010;29:938-949.

    Article  Google Scholar 

  7. Presotto L, Gianolli L, Gilardi MC, Bettinardi V. Evaluation of image reconstruction algorithms encompassing Time-Of-Flight and Point Spread Function modelling for quantitative cardiac PET: Phantom studies. J Nucl Cardiol 2015;22:351-63.

    Article  CAS  Google Scholar 

  8. DiFilippo FP, Brunken RC. Impact of time-of-flight reconstruction on cardiac PET images of obese patients. Clin Nucl Med 2017;42:e103-e108.

    Article  Google Scholar 

  9. Floberg JM, Struck AF, Peters BK, Jaskowiak CJ, Perlman SB, Hall LT. Impact of expectation-maximization reconstruction iterations on the diagnosis of temporal lobe epilepsy with PET. Am J Nucl Med Mol Imaging 2012;2:335-43.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Morey AM, Kadrmas DJ. Effect of varying number of OSEM subsets on PET lesion detectability. J Nucl Med Technol 2013;41:268-73.

    Article  Google Scholar 

  11. Murthy VL, Bateman TM, Beanlands RS, Berman DS, Borges-Neto 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 2017;59:1-29.

    Google Scholar 

  12. Lee BC, Moody JB, Weinberg RL, Corbett JR, Ficaro EP, Murthy VL. Optimization of temporal sampling for 82rubidium PET myocardial blood flow quantification. J Nucl Cardiol 2017;24:1517-29.

    Article  Google Scholar 

  13. Armstrong IS, Tonge CM, Arumugam P. Impact of point spread function modeling and time-of-flight on myocardial blood flow and myocardial flow reserve measurements for rubidium-82 cardiac PET. J Nucl Cardiol 2014;21:467-74.

    Article  Google Scholar 

  14. Armstrong IS, Tonge CM, Arumugam P. Assessing time-of-flight signal-to-noise ratio gains within the myocardium and subsequent reductions in administered activity in cardiac PET studies. J Nucl Cardiol 2019;26:405-12.

    Article  Google Scholar 

  15. Dasari PKR, Jones JP, Casey ME, Smith MF. The effect of time-of-flight and point spread function modeling on quantitative cardiac PET of large patients: Phantom Studies. IEEE Trans Radiat Plasma Med Sci 2017;1:416-425.

    Article  Google Scholar 

  16. Genders TSS, Steyerberg EW, Hunink MGM, Nieman K, Galema TW, Mollet NR, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. Bmj 2012;344:e3485.

  17. Jakoby BW, Bercier Y, Conti M, Casey ME, Bendriem B, Townsend DW. Physical and clinical performance of the mCT time-of-flight PET/CT scanner. Phys Med Biol 2011;56:2375-89.

    Article  CAS  Google Scholar 

  18. 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 

  19. Lee BC, Moody JB, Poitrasson-Rivière 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. November 2018:1–17.

  20. Lortie M, Beanlands RSB, Yoshinaga K, Klein R, DaSilva JN, deKemp RA. Quantification of myocardial blood flow with 82Rb dynamic PET imaging. Eur J Nucl Med Mol Imaging 2007;34:1765-74.

    Article  Google Scholar 

  21. Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw 2015;67.

  22. 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.

    Article  Google Scholar 

  23. Prior JO, Allenbach G, Valenta I, Kosinski M, Burger C, Verdun FR, et al. Quantification of myocardial blood flow with 82Rb positron emission tomography: Clinical validation with 15O-water. Eur J Nucl Med Mol Imaging 2012;39:1037-47.

    Article  Google Scholar 

  24. Nuyts J, Maes, A, Vrolix M, Schiepers C, Schelbert H, Kuhle W, et al. Three-dimensional correction for spillover and recovery of myocardial PET images. J Nucl Med 1996;37:767-74.

    CAS  PubMed  Google Scholar 

  25. Hutchins GD, Caraher JM, Raylman RR. A region of interest strategy for minimizing resolution distortions in quantitative myocardial PET studies. J Nucl Med 1992;33:1243-50.

    CAS  PubMed  Google Scholar 

  26. Renaud JM, Dasilva JN, Beanlands RSB, Dekemp RA. Characterizing the normal range of myocardial blood flow with 82rubidium and 13N-ammonia PET imaging. J Nucl Cardiol 2013;20:578-91.

    Article  Google Scholar 

  27. Steffen DA, Giannopoulos AA, Grossmann M, Messerli M, Schwyzer M, Grani C, et al. “Apical thinning”: Relations between myocardial wall thickness and apical left ventricular tracer uptake as assessed with positron emission tomography myocardial perfusion imaging. J Nucl Cardiol 2020;27:452-60.

    Article  Google Scholar 

  28. 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 

  29. Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. Physical performance of the new hybrid PETCT discovery-690. Med Phys 2011;38:5394-5411.

    Article  CAS  Google Scholar 

Download references

Disclosure

A. Poitrasson-Rivière, J.B. Moody, T. Hagio, and J.M. Renaud are employees of INVIA. J.M. Renaud is a consultant for Jubilant DraxImage and receives royalties from the sales of FlowQuant® software. L. Arida-Moody has nothing to disclose. V.L. Murthy is supported by 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 as well as the Melvyn Rubenfire Professorship in Preventive Cardiology. Dr. Murthy has received research grants and speaking honoraria from Siemens Medical Imaging. He serves as a scientific advisor for Ionetix and owns stock options in the same. Dr. Murthy also owns stock in General Electric and Cardinal Health. He has received expert witness payments on behalf of Jubilant Draximage and a speaking honorarium from 2Quart Medical. Dr. Murthy receives non-financial research support from INVIA Medical Imaging Solutions. E.P. Ficaro is a stockholder of INVIA, which produces Corridor4DM, a clinical software package for nuclear cardiology.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexis Poitrasson-Rivière PhD.

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 summarises the contents of the paper and is free for re-use at meetings and presentations. Search for the article DOI on SpringerLink.com.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 872 kb)

Supplementary file2 (PPTX 1526 kb)

Rights and permissions

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. Effect of iterations and time of flight on normal distributions of 82Rb PET relative perfusion and myocardial blood flow. J. Nucl. Cardiol. 29, 2612–2623 (2022). https://doi.org/10.1007/s12350-021-02775-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12350-021-02775-8

Keywords

Navigation