Quantitative assessment of myocardial blood flow and extracellular volume fraction using 68Ga-DOTA-PET: A feasibility and validation study in large animals

  • Carlos Velasco
  • Adriana Mota-Cobián
  • Rubén A. Mota
  • Juan Pellico
  • Fernando Herranz
  • Carlos Galán-Arriola
  • Borja Ibáñez
  • Jesús Ruiz-Cabello
  • Jesús MateoEmail author
  • Samuel España
Original Article



Here we evaluated the feasibility of PET with Gallium-68 (68Ga)-labeled DOTA for non-invasive assessment of myocardial blood flow (MBF) and extracellular volume fraction (ECV) in a pig model of myocardial infarction. We also aimed to validate MBF measurements using microspheres as a gold standard in healthy pigs.


8 healthy pigs underwent three sequential 68Ga-DOTA-PET/CT scans at rest and during pharmacological stress with simultaneous injection of fluorescent microspheres to validate MBF measurements. Myocardial infarction was induced in 5 additional pigs, which underwent 68Ga-DOTA-PET/CT examinations 7-days after reperfusion. Dynamic PET images were reconstructed and fitted to obtain MBF and ECV parametric maps.


MBF assessed with 68Ga-DOTA-PET showed good correlation (y = 0.96x + 0.11, r = 0.91) with that measured with microspheres. MBF values obtained with 68Ga-DOTA-PET in the infarcted area (LAD, left anterior descendant) were significantly reduced in comparison to remote ones LCX (left circumflex artery, P < 0.0001) and RCA (right coronary artery, P < 0.0001). ECV increased in the infarcted area (P < 0.0001).


68Ga-DOTA-PET allowed non-invasive assessment of MBF and ECV in pigs with myocardial infarction and under rest-stress conditions. This technique could provide wide access to quantitative measurement of both MBF and ECV with PET imaging.


PET myocardial blood flow perfusion agents tracers molecular imaging agents molecular imaging 



Coronary artery disease




Cardiac magnetic resonance


Computed tomography


1,4,7,10-Tetraazacyclododecane-1,4,7,10-tetraacetic acid


Extracellular volume fraction


Myocardial blood flow




Positron emission tomography



The authors would like to thank Dr. Stuart Pocock (London School of Hygiene and Tropical Medicine, UK) for his help and advice regarding statistical analysis.


All authors have reported that they have no relationships relevant to the contents of this paper to disclose. This work was supported by grants from the Ministerio de Economía, Industria y Competitividad (MEIC) (SAF2014-58920-R), from the Carlos III Institute of Health of Spain and Fondo Europeo de Desarrollo Regional (FEDER, “Una manera de hacer Europa”) (FIS-FEDER PI14-01427), and from the Comunidad de Madrid (2016-T1/TIC-1099). C. Velasco holds a fellowship from the Spanish Ministry of Education (FPU014/01794). The CNIC is supported by the Ministerio de Ciencia, Innovación y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). This work was performed under the Maria de Maeztu Units of Excellence Program from the Spanish State Research Agency (MDM-2017-0720).

Supplementary material

12350_2019_1694_MOESM1_ESM.docx (38 kb)
Supplementary material 1 (DOCX 38 kb)
12350_2019_1694_MOESM2_ESM.pptx (3.3 mb)
Supplementary material 2 (PPTX 3387 kb)


  1. 1.
    Benjamin EJ, Blaha MJ, Chiuve SE, et al (2017) Heart disease and stroke statistics’ 2017 Update: A Report from the American Heart Association.Google Scholar
  2. 2.
    Schindler TH, Schelbert HR, Quercioli A, Dilsizian V. Cardiac PET imaging for the detection and monitoring of coronary artery disease and microvascular health. JACC: Cardiovasc Imaging. 2010;3:623–40.Google Scholar
  3. 3.
    Dilsizian V, Bacharach SL, Beanlands RS, et al. PET myocardial perfusion and metabolism clinical imaging. J Nucl Cardiol. 2009;16:651.CrossRefGoogle Scholar
  4. 4.
    Heller GV, Calnon D, Dorbala S. Recent advances in cardiac PET and PET/CT myocardial perfusion imaging. J Nucl Cardiol. 2009;16:962–9.CrossRefGoogle Scholar
  5. 5.
    Packard RRS, Huang S-C, Dahlbom M, et al. Absolute quantitation of myocardial blood flow in human subjects with or without myocardial ischemia using dynamic flurpiridaz F 18 PET. J Nucl Med. 2014;55:1438–45.CrossRefPubMedCentralGoogle Scholar
  6. 6.
    Breeman WAP, Verbruggen AM. The 68Ge/68Ga generator has high potential, but when can we use 68Ga-labelled tracers in clinical routine? Eur J Nucl Med Mol Imaging. 2007;34:978–81.CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Shetty D, Lee YS, Jeong JM. 68Ga-labeled radiopharmaceuticals for positron emission tomography. Nucl Med Mol Imaging. 2010;44:233–40.CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Tarkia M, Saraste A, Saanijoki T, et al. Evaluation of 68Ga-labeled tracers for PET imaging of myocardial perfusion in pigs. Nucl Med Biol. 2012;39:715–23.CrossRefGoogle Scholar
  9. 9.
    Ley S, Ley-Zaporozhan J. Pulmonary perfusion imaging using MRI: Clinical application. Insights Imaging. 2012;3:61–71.CrossRefGoogle Scholar
  10. 10.
    Velasco C, Mateo J, Santos A, et al. Assessment of regional pulmonary blood flow using 68Ga-DOTA PET. EJNMMI Res. 2017;7:7.CrossRefPubMedCentralGoogle Scholar
  11. 11.
    Autio A, Saraste A, Kudomi N, et al. Assessment of blood flow with (68)Ga-DOTA PET in experimental inflammation: A validation study using (15)O-water. Am J Nucl Med Mol Imaging. 2014;4:571–9.PubMedCentralGoogle Scholar
  12. 12.
    Medical Advisory Secretariat. Positron emission tomography for the assessment of myocardial viability: An evidence-based analysis. Ont Health Technol Assess Ser. 2005;5:1–167.Google Scholar
  13. 13.
    Ugander M, Oki AJ, Hsu LY, et al. Extracellular volume imaging by magnetic resonance imaging provides insights into overt and sub-clinical myocardial pathology. Eur Heart J. 2012;33:1268–78.CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Kim H, Lee SJ, Davies-Venn C, et al. 64Cu-DOTA as a surrogate positron analog of Gd-DOTA for cardiac fibrosis detection with PET: Pharmacokinetic study in a rat model of chronic MI. Nucl Med Commun. 2016;37:188–96.CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Glenny RW, Bernard S, Brinkley M. Validation of fluorescent-labeled microspheres for measurement of regional organ perfusion. J Appl Physiol. 1993;74:2585–97.CrossRefGoogle Scholar
  16. 16.
    Fernández-Jiménez R, García-Prieto J, Sánchez-González J, et al. Pathophysiology underlying the bimodal edema phenomenon after myocardial ischemia/reperfusion. J Am Coll Cardiol. 2015;66:816–28.CrossRefGoogle Scholar
  17. 17.
    Hein TW, Belardinelli L, Kuo L. Adenosine A(2A) receptors mediate coronary microvascular dilation to adenosine: Role of nitric oxide and ATP-sensitive potassium channels. J Pharmacol Exp Ther. 1999;291:655–64.Google Scholar
  18. 18.
    Aime S, Caravan P. Biodistribution of gadolinium-based contrast agents. Incl Gadolinium Depos. 2009;1267:1259–67.Google Scholar
  19. 19.
    Sourbron SP, Buckley DL. On the scope and interpretation of the tofts models for DCE-MRI. Magnetic Resonance in Medicine. 2011;745:735–45.CrossRefGoogle Scholar
  20. 20.
    Nordström J, Kero T, Harms HJ, et al. Calculation of left ventricular volumes and ejection fraction from dynamic cardiac-gated 15O-water PET/CT: 5D-PET. EJNMMI Phys. 2017;4:26.CrossRefPubMedCentralGoogle Scholar
  21. 21.
    van der Weerdt AP, Klein LJ, Boellaard R, et al. Image-derived input functions for determination of MRGlu in cardiac (18)F-FDG PET scans. J Nucl Med. 2001;42:1622–9.Google Scholar
  22. 22.
    Nesterov SV, Han C, Mäki M, et al. Myocardial perfusion quantitation with15O-labelled water PET: High reproducibility of the new cardiac analysis software (CarimasTM). Eur J Nucl Med Mol Imaging. 2009;36:1594–602.CrossRefGoogle Scholar
  23. 23.
    Cerqueira MD, Weissman NJ, Dilsizian V, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: A statement for healthcare professionals from the cardiac imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circ J Am Hear Assoc. 2002;105:539–42.Google Scholar
  24. 24.
    Renkin EM. Transport of potassium-42 from blood to tissue in isolated mammalian skeletal muscles. Am J Physiol. 1959;197:1205–10.CrossRefGoogle Scholar
  25. 25.
    Crone C. The permeability of capillaries in various organs as determined by use of the “Indicator Diffusion” method. Acta Physiol Scand. 1963;58:292–305.CrossRefGoogle Scholar
  26. 26.
    Fernández-Jiménez R, Galán-Arriola C, Sánchez-González J, et al. Effect of ischemia duration and protective interventions on the temporal dynamics of tissue composition after myocardial infarction. Circ Res. 2017;121:439–50.CrossRefPubMedCentralGoogle Scholar
  27. 27.
    Serrat MA. Measuring bone blood supply in mice using fluorescent microspheres. Nat Protoc. 2009;4:1749–58.CrossRefGoogle Scholar
  28. 28.
    Fan FC, Schuessler GB, Chen RY, Chien S. Determinations of blood flow and shunting of 9- and 15-micrometer spheres in regional beds. Am J Physiol: Hear Circ Physiol. 1979;237:H25–33.Google Scholar
  29. 29.
    Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307–10.CrossRefGoogle Scholar
  30. 30.
    McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods. 1996;1:30–46.CrossRefGoogle Scholar
  31. 31.
    Harms HJ, Nesterov SV, Han C, et al. Comparison of clinical non-commercial tools for automated quantification of myocardial blood flow using oxygen-15-labelled water PET/CT. Eur Heart J Cardiovasc Imaging. 2014;15:431–41.CrossRefGoogle Scholar
  32. 32.
    White SK, Sado DM, Flett AS, Moon JC. Characterising the myocardial interstitial space: The clinical relevance of non-invasive imaging. Heart. 2012;98:773–9.CrossRefGoogle Scholar
  33. 33.
    Maddahi J, Packard RRS. Cardiac PET perfusion tracers: Current status and future directions. Semin Nucl Med. 2014;44:333–43.CrossRefPubMedCentralGoogle Scholar
  34. 34.
    Jaarsma C, Leiner T, Bekkers SC, et al. Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease: A meta-anal. J Am Coll Cardiol. 2012;59:1719–28.CrossRefGoogle Scholar
  35. 35.
    Sampson UK, Dorbala S, Limaye A, et al. 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.CrossRefGoogle Scholar

Copyright information

© American Society of Nuclear Cardiology 2019

Authors and Affiliations

  • Carlos Velasco
    • 1
    • 2
  • Adriana Mota-Cobián
    • 1
    • 2
  • Rubén A. Mota
    • 1
    • 3
  • Juan Pellico
    • 1
    • 8
  • Fernando Herranz
    • 1
    • 8
  • Carlos Galán-Arriola
    • 1
    • 4
  • Borja Ibáñez
    • 1
    • 4
    • 5
  • Jesús Ruiz-Cabello
    • 2
    • 6
    • 7
    • 8
  • Jesús Mateo
    • 1
    Email author
  • Samuel España
    • 1
    • 2
    • 9
  1. 1.Centro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
  2. 2.Universidad Complutense de MadridMadridSpain
  3. 3.Charles River Laboratories EspañaCerdanyolaSpain
  4. 4.CIBER de enfermedades CardiovascularesMadridSpain
  5. 5.Cardiology DepartmentIIS-Fundación Jiménez Díaz HospitalMadridSpain
  6. 6.CIC biomaGUNESan Sebastian-DonostiaSpain
  7. 7.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain
  8. 8.CIBER de Enfermedades RespiratoriasMadridSpain
  9. 9.Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC)MadridSpain

Personalised recommendations