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Quantification of regional myocardial blood flow estimation with three-dimensional dynamic rubidium-82 PET and modified spillover correction model

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Journal of Nuclear Cardiology Aims and scope



Myocardial blood flow (MBF) estimation with 82Rubidium (82Rb) positron emission tomography (PET) is technically difficult because of the high spillover between regions of interest, especially due to the long positron range. We sought to develop a new algorithm to reduce the spillover in image-derived blood activity curves, using non-uniform weighted least-squares fitting.


Fourteen volunteers underwent imaging with both 3-dimensional (3D) 82Rb and 15O-water PET at rest and during pharmacological stress. Whole left ventricular (LV) 82Rb MBF was estimated using a one-compartment model, including a myocardium-to-blood spillover correction to estimate the corresponding blood input function Ca(t)whole. Regional K1 values were calculated using this uniform global input function, which simplifies equations and enables robust estimation of MBF. To assess the robustness of the modified algorithm, inter-operator repeatability of 3D 82Rb MBF was compared with a previously established method.


Whole LV correlation of 82Rb MBF with 15O-water MBF was better (P < .01) with the modified spillover correction method (r = 0.92 vs r = 0.60). The modified method also yielded significantly improved inter-operator repeatability of regional MBF quantification (r = 0.89) versus the established method (r = 0.82) (P < .01).


A uniform global input function can suppress LV spillover into the image-derived blood input function, resulting in improved precision for MBF quantification with 3D 82Rb PET.

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  1. Manabe O, Yoshinaga K, Katoh C, Naya M, DeKemp RA, Tamaki N. Repeatability of rest and hyperemic myocardial blood flow measurements with 82Rb dynamic PET. J Nucl Med 2009;50:68-71.

    Article  PubMed  Google Scholar 

  2. Lortie M, Beanlands RS, 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  PubMed  Google Scholar 

  3. Yoshinaga K, Manabe O, Katoh C, Chen L, Klein R, Naya M, et al. Quantitative analysis of coronary endothelial function with generator-produced 82Rb PET: Comparison with 15O-labelled water PET. Eur J Nucl Med Mol Imaging 2010;37:2233-41.

    Article  PubMed  Google Scholar 

  4. Lin JW, Sciacca RR, Chou RL, Laine AF, Bergmann SR. Quantification of myocardial perfusion in human subjects using 82Rb and wavelet-based noise reduction. J Nucl Med 2001;42:201-8.

    PubMed  CAS  Google Scholar 

  5. 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  PubMed  Google Scholar 

  6. Yoshinaga K, Klein R, Tamaki N. Generator-produced rubidium-82 positron emission tomography myocardial perfusion imaging: From basic aspects to clinical applications. J Cardiol 2010;55:163-73.

    Article  PubMed  Google Scholar 

  7. Yoshinaga K, Chow BJ, deKemp RA, Thorn S, Ruddy TD, Davies RA, et al. Application of cardiac molecular imaging using positron emission tomography in evaluation of drug and therapeutics for cardiovascular disorders. Curr Pharm 2005;11:903-32.

    Article  CAS  Google Scholar 

  8. Klein R, Beanlands RS, deKemp RA. Quantification of myocardial blood flow and flow reserve—technical aspects. J Nucl Cardiol 2010;17:555-70.

    Article  PubMed  Google Scholar 

  9. Slomka PJ, Le Meunier L, Hayes SW, Acampa W, Oba M, Haemer GG, et al. Comparison of myocardial perfusion 82Rb PET performed with CT- and transmission CT-based attenuation correction. J Nucl Med 2008;49:1992-8.

    Article  PubMed  Google Scholar 

  10. Bateman TM, Heller GV, McGhie AI, Friedman JD, Case JA, Bryngelson JR, et al. Diagnostic accuracy of rest/stress ECG-gated Rb-82 myocardial perfusion PET: Comparison with ECG-gated Tc-99m sestamibi SPECT. J Nucl Cardiol 2006;13:24-33.

    Article  PubMed  Google Scholar 

  11. Beanlands RS, Chow BJ, Dick A, Friedrich MG, Gulenchyn KY, Kiess M, et al. CCS/CAR/CANM/CNCS/CanSCMR joint position statement on advanced noninvasive cardiac imaging using positron emission tomography, magnetic resonance imaging and multidetector computed tomographic angiography in the diagnosis and evaluation of ischemic heart disease—executive summary. Can J Cardiol 2007;23:107-19.

    Article  PubMed  CAS  Google Scholar 

  12. Sampson UK, Dorbala S, Limaye A, Kwong R, Di Carli MF. Diagnostic accuracy of rubidium-82 myocardial perfusion imaging with hybrid positron emission tomography/computed tomography in the detection of coronary artery disease. J Am Coll Cardiol 2007;49:1052-8.

    Article  PubMed  CAS  Google Scholar 

  13. Lertsburapa K, Ahlberg AW, Bateman TM, Katten D, Volker L, Cullom SJ, et al. Independent and incremental prognostic value of left ventricular ejection fraction determined by stress gated rubidium 82 PET imaging in patients with known or suspected coronary artery disease. J Nucl Cardiol 2008;15:745-53.

    PubMed  Google Scholar 

  14. Yoshinaga K, Chow BJ, Williams K, Chen L, deKemp RA, Garrard L, et al. What is the prognostic value of myocardial perfusion imaging using rubidium-82 positron emission tomography? J Am Coll Cardiol 2006;48:1029-39.

    Article  PubMed  Google Scholar 

  15. Bengel FM, Higuchi T, Javadi MS, Lautamaki R. Cardiac positron emission tomography. J Am Coll Cardiol 2009;54:1-15.

    Article  PubMed  Google Scholar 

  16. Yoshinaga K, Tamaki N, Ruddy T, DeKemp RA, Beanlands R. Evaluation of myocardial perfusion. Principles and practice of PET and PET/CT. 2nd ed., 2009Philadelphia, PA: Lippincott Williams and Wilkins; 2009. p. 541-64.

    Google Scholar 

  17. Yoshinaga K, Katoh C, Noriyasu K, Iwado Y, Furuyama H, Ito Y, et al. Reduction of coronary flow reserve in areas with and without ischemia on stress perfusion imaging in patients with coronary artery disease: A study using oxygen 15-labeled water PET. J Nucl Cardiol 2003;10:275-83.

    Article  PubMed  Google Scholar 

  18. Parkash R, deKemp RA, Ruddy TD, Kitsikis A, Hart R, Beauchesne L, et al. Potential utility of rubidium 82 PET quantification in patients with 3-vessel coronary artery disease. J Nucl Cardiol 2004;11:440-9.

    Article  PubMed  CAS  Google Scholar 

  19. Camici PG, Crea F. Coronary microvascular dysfunction. N Engl J Med 2007;356:830-40.

    Article  PubMed  CAS  Google Scholar 

  20. Klein R, Renaud JM, Ziadi MC, Thorn SL, Adler A, Beanlands RS, et al. Intra- and inter-operator repeatability of myocardial blood flow and myocardial flow reserve measurements using Rubidium-82 PET and a highly automated analysis program. J Nucl Cardiol 2010;17:600-16.

    Article  PubMed  Google Scholar 

  21. Lautamäki R, George RT, Kitagawa K, Higuchi T, Merrill J, Voicu C, et al. Rubidium-82 PET-CT for quantitative assessment of myocardial blood flow: Validation in a canine model of coronary artery stenosis. Eur J Nucl Med Mol Imaging 2009;36:576-86.

    Article  PubMed  Google Scholar 

  22. Schepis T, Gaemperli O, Treyer V, Valenta I, Burger C, Koepfli P, et al. Absolute quantification of myocardial blood flow with 13N-ammonia and 3-dimensional PET. J Nucl Med 2007;48:1783-9.

    Article  PubMed  CAS  Google Scholar 

  23. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med 1979;300:1350-8.

    Article  PubMed  CAS  Google Scholar 

  24. Naya M, Tsukamoto T, Morita K, Katoh C, Furumoto T, Fujii S, et al. Olmesartan, but not amlodipine, improves endothelium-dependent coronary dilation in hypertensive patients. J Am Coll Cardiol 2007;50:1144-9.

    Article  PubMed  CAS  Google Scholar 

  25. Siegrist PT, Gaemperli O, Koepfli P, Schepis T, Namdar M, Valenta I, et al. Repeatability of cold pressor test-induced flow increase assessed with H(2)(15)O and PET. J Nucl Med 2006;47:1420-6.

    PubMed  Google Scholar 

  26. Furuyama H, Odagawa Y, Katoh C, Iwado Y, Yoshinaga K, Ito Y, et al. Assessment of coronary function in children with a history of Kawasaki disease using (15)O-water positron emission tomography. Circulation 2002;105:2878-84.

    Article  PubMed  Google Scholar 

  27. Herrero P, Markham J, Shelton ME, Bergmann SR. Implementation and evaluation of a two-compartment model for quantification of myocardial perfusion with rubidium-82 and positron emission tomography. Circ Res 1992;70:496-507.

    Article  PubMed  CAS  Google Scholar 

  28. Katoh C, Morita K, Shiga T, Kubo N, Nakada K, Tamaki N. Improvement of algorithm for quantification of regional myocardial blood flow using 15O-water with PET. J Nucl Med 2004;45:1908-16.

    PubMed  Google Scholar 

  29. Yoshinaga K, Katoh C, Manabe O, Klein R, Naya M, Sakakibara M, 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.

    Article  PubMed  Google Scholar 

  30. deKemp RA, Yoshinaga K, Beanlands RS. Will 3-dimensional PET-CT enable the routine quantification of myocardial blood flow? J Nucl Cardiol 2007;14:380-97.

    Article  PubMed  Google Scholar 

  31. Nesterov SV, Han C, Mäki M, Kajander S, Naum AG, Helenius H, et al. Myocardial perfusion quantitation with 15O-labelled water PET: High reproducibility of the new cardiac analysis software (Carimas). Eur J Nucl Med Mol Imaging 2009;36:1594-602.

    Article  PubMed  Google Scholar 

  32. Schindler TH, Zhang XL, Prior JO, Cadenas J, Dahlbom M, Sayre J, et al. Assessment of intra- and interobserver reproducibility of rest and cold pressor test-stimulated myocardial blood flow with (13)N-ammonia and PET. Eur J Nucl Med Mol Imaging 2007;34:1178-88.

    Article  PubMed  Google Scholar 

  33. Esteves FP, Nye JA, Khan A, Folks RD, Halkar RK, Garcia EV, et al. Prompt-gamma compensation in Rb-82 myocardial perfusion 3D PET/CT. J Nucl Cardiol 2010;17:247-53.

    Article  PubMed  Google Scholar 

  34. Machac J, Bacharach SL, Bateman TM, Bax JJ, Beanlands R, Bengel F, et al. Positron emission tomography myocardial perfusion and glucose metabolism imaging. J Nucl Cardiol 2006;13:e121-51.

    Article  PubMed  Google Scholar 

  35. Alessio AM, Kinahan PE, Champley KM. Attenuation-emission alignment in cardiac PET/CT based on consistency conditions. Med Phys 2010;37:1191-200.

    Article  PubMed  Google Scholar 

  36. Hunt DC, Easton H, Caldwell CB. Design and construction of a quality control phantom for SPECT and PET imaging. Med Phys 2009;36:5404-11.

    Article  PubMed  Google Scholar 

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The authors thank Sayaka Takamori, RT; Keiichi Magota, RT; Hiroshi Arai, RT; Hidehiko Omote, RT; Kyotaro Suzuma, MS; and Ken-ichi Nishijima, PhD, for their technical expertise, and Eriko Suzuki for her administrative support of this study. This study was supported in part by grants from the Ministry of Education, Science and Culture (No. 19591395), Northern Advancement Center for Science & Technology (Sapporo, Japan) (Grant #H19-C-068, H23-S2-17), and Adult Cardiovascular Research Foundation (Kyoto Japan). Ran Klein was supported by the Japan Society for the Promotion of Science (JSPS) and Natural Sciences and Engineering Research Council of Canada (NSERC) Summer Program (2008) (Tokyo, Japan and Ottawa, Ontario, Canada).

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Correspondence to Keiichiro Yoshinaga MD, PhD, FACC.

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This study was supported in part by Grants from the Ministry of Education, Science and Culture (No. 19591395), Northern Advancement Center for Science & Technology (Sapporo, Japan) (#H19-C-068, H23-S2-17), and Adult Cardiovascular Foundation (Kyoto, Japan). Ran Klein was supported by the Japan Society for the Promotion of Science (JSPS) and the Natural Sciences and Engineering Research Council of Canada (NSERC) Summer Program (2008) (Tokyo, Japan and Ottawa, Ontario, Canada).



15O-Water Model

The myocardium was modeled as a partial-volume mixture of arterial blood Ca(t) and tissue Ct(t) activity concentrations as in Eq. 1 (see below) where PTF denoted perfusable tissue fraction, VA is the fractional arterial blood volume, and ρ is the density of tissue (1.04 g/ml).

$$ {\text{R}}\left( t \right) = {\text{PTF}} \cdot \rho \cdot {\text{Ct}}\left( {\text{t}} \right) + {\text{VA}} \cdot {\text{Ca}}\left( {\text{t}} \right) $$

The change in tissue activity concentration was modeled using the 1-tissue compartment model in Eq. 2 (see below) where F denotes blood flow in mL/minute/g. The parameter ρ is the partition coefficient of water in the myocardium and is equal to 0.91.

$$ {\text{dCt}}\left( t \right) / {\text{d}}t = F \cdot {\text{Ca}}\left( t \right)-(F/\rho) \cdot {\text{Ct}}\left( t \right) $$

In the LV blood cavity, activity concentration was modeled as a partial-volume mixture of β = 85% arterial blood and (1 - β = 15%) myocardial tissue as shown in Eq. 3.

$$ {\text{LV}}\left( {\text{t}} \right) = \beta \cdot {\text{Ca}}\left( {\text{t}} \right) + ( 1- \beta ) \cdot \rho \cdot {\text{Ct}}\left( {\text{t}} \right) $$

Equations 1, 2, and 3 were solved with a nonlinear least-squares analysis to estimate PTF, VA, and F, which was used as the estimate of MBF.

82Rb Models

Established method

The measured tissue TAC in each myocardial ROI, R(t), during the entire scan length was estimated using Eq. 1. The change in tissue activity concentration was modeled using the one-tissue compartment model

$$ {\text{dCt}}\left( {\text{t}} \right) /{\text{dt}} = K1 \cdot {\text{Ca}}\left( {\text{t}} \right) -k2 \cdot {\text{Ct}}\left( {\text{t}} \right) $$

where K1 (mL/minute/g) is the uptake rate from blood into the tissue and k2 (/minute) is the washout rate from myocardial tissue into the blood Ca(t) (Bq/mL). Radioactivity in the LV blood pool was calculated using Eq. 3 with β = 85%.

The parameters PTF, VA, K1, and k2 were derived by nonlinear least-squares minimization using Eqs. 1, 3, and 4, where PTF represents the tissue fraction in the LV myocardium ROI.

Thus, the spillover-corrected pure blood Ca(t) for each segment was estimated. Conversion from K1 to MBF was estimated with the modified Renkin-Crone model1,27 as shown in Eq. 5.

$$ K1 = {\text{MBF}}\left[ { 1- 0.86 {\text{ exp }}\left( { -0.543/{\text{MBF}}} \right)} \right] $$

Modified or dual-spillover method

Regional myocardial ROI data, R(t), were then analyzed using Eqs. 6 and 7.

$$ {\text{R}}\left( {\text{t}} \right) = {\text{PTF}} \cdot \rho \cdot {\text{Ct}}\left( {\text{t}} \right) + {\text{VA}} \cdot {\text{Ca}}^{\text{whole}} \left( {\text{t}} \right) $$
$$ {\text{dCt}}\left( {\text{t}} \right) /{\text{ dt }} = K1 \cdot {\text{Ca}}^{\text{whole}} \left( {\text{t}} \right) -k2 \cdot {\text{Ct}}\left( {\text{t}} \right) $$

The regional myocardial ROI curve and left ventricular ROI blood curve (R(t) and LV(t), respectively) were sampled from the dynamic image as in the established conventional method and were assumed to be a linear combination of the uniform blood Cawhole(t) and myocardium Ct(t) as in Eqs. 1 and 3. In addition, the relationship between Cawhole(t) and Ct(t) was defined by the one-tissue-compartment model as in Eq. 7.

The parameters PTF, VA, K1, and k2 were simultaneously estimated by minimizing the weighted error (ε) as shown in Eq. 8 between the measured curve R(t) and the model in Eq. 6. The error was weighted by the blood activity concentration Cawhole(t) to enforce strict tolerance in the curve fitting during the timeframes corresponding to high radioactivity in the blood.

$$ \varepsilon = \Upsigma [{\text{Ca}}^{\text{whole}} \left( {\text{t}} \right) \cdot ({\text{PTF}} \cdot \rho \cdot {\text{Ct}}\left( {\text{t}} \right) + {\text{VA}} \cdot {\text{Ca}}^{\text{whole}} \left( {\text{t}} \right) - {\text{R}}\left( {\text{t}} \right))]^{ 2} $$

K1 values were converted into MBF using the Renkin-Crone extraction function as in Eq. 5.3

The estimated MBF values from the established uniform and modified weighted methods were compared against MBF values estimated from the corresponding 15O-water images.

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Katoh, C., Yoshinaga, K., Klein, R. et al. Quantification of regional myocardial blood flow estimation with three-dimensional dynamic rubidium-82 PET and modified spillover correction model. J. Nucl. Cardiol. 19, 763–774 (2012).

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