Quantification of Positron Emission Tomography Data Using Simultaneous Estimation of the Input Function: Validation with Venous Blood and Replication of Clinical Studies

  • Elizabeth A. BartlettEmail author
  • Mala Ananth
  • Samantha Rossano
  • Mengru Zhang
  • Jie Yang
  • Shu-fei Lin
  • Nabeel Nabulsi
  • Yiyun Huang
  • Francesca Zanderigo
  • Ramin V. Parsey
  • Christine DeLorenzo
Research Article



To determine if one venous blood sample can substitute full arterial sampling in quantitative modeling for multiple positron emission tomography (PET) radiotracers using simultaneous estimation of the input function (SIME).


Participants underwent PET imaging with [11C]ABP688, [11C]CUMI-101, and [11C]DASB. Full arterial sampling and additional venous blood draws were performed for quantification with the arterial input function (AIF) and SIME using one arterial or venous (vSIME) sample.


Venous and arterial metabolite-corrected plasma activities were within 6 % of each other at varying time points. vSIME- and AIF-derived outcome measures were in good agreement, with optimal sampling times of 12 min ([11C]ABP688), 90 min ([11C]CUMI-101), and 100 min ([11C]DASB). Simulation-based power analyses revealed that SIME required fewer subjects than the AIF method to achieve statistical power, with significant reductions for [11C]CUMI-101 and [11C]DASB with vSIME. Replication of previous findings and test-retest analyses bolstered the simulation analyses.


We demonstrate the feasibility of AIF recovery using SIME with one venous sample for [11C]ABP688, [11C]CUMI-101, and [11C]DASB. This method simplifies PET acquisition while allowing for fully quantitative modeling, although some variability and bias are present with respect to AIF-based quantification, which may depend on the accuracy of the single venous blood measurement.

Key words

Venous blood Less invasive PET Simultaneous estimation Sample size considerations 



We thank the Center for Understanding Biology using Imaging Technology image analysts at Stony Brook University for their work in image importing, processing, and quality control. We also thank Rajapillai Pillai, PhD, for his contributions to early versions of the paper. We acknowledge the consultation and support provided by the Biostatistical Consulting Core at the Stony Brook University School of Medicine.


This study was funded by the National Institute of Mental Health awards: K01MH091354 (PI: Christine DeLorenzo, PhD), R01MH104512 (PI: Christine DeLorenzo, PhD), and R01MH090276 (PI: Ramin V Parsey, MD, PhD).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki Declaration and its later amendments.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11307_2018_1300_MOESM1_ESM.pdf (338 kb)
ESM 1 (PDF 338 kb)


  1. 1.
    Innis RB, Cunningham VJ, Delforge J et al (2007) Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 27:1533–1539CrossRefPubMedGoogle Scholar
  2. 2.
    Ogden RT, Zanderigo F, Choy S et al (2010) Simultaneous estimation of input functions: an empirical study. J Cereb Blood Flow Metab 30:816–826CrossRefPubMedGoogle Scholar
  3. 3.
    Parsey RV, Kent JM, Oquendo MA et al (2006) Acute occupancy of brain serotonin transporter by sertraline as measured by [11C]DASB and positron emission tomography. Biol Psychiatry 59:821–828CrossRefPubMedGoogle Scholar
  4. 4.
    Ametamey SM, Treyer V, Streffer J et al (2007) Human PET studies of metabotropic glutamate receptor subtype 5 with 11C-ABP688. J Nucl Med 48:247–252PubMedGoogle Scholar
  5. 5.
    DeLorenzo C, Kumar JS, Zanderigo F et al (2009) Modeling considerations for in vivo quantification of the dopamine transporter using [(11)C]PE2I and positron emission tomography. J Cereb Blood Flow Metab 29:1332–1345CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Ishibashi K, Robertson CL, Mandelkern MA, et al. (2013) The simplified reference tissue model with 18F-fallypride positron emission tomography: choice of reference region. Mol Imaging 12Google Scholar
  7. 7.
    Takikawa S, Dhawan V, Spetsieris P et al (1993) Noninvasive quantitative fluorodeoxyglucose PET studies with an estimated input function derived from a population-based arterial blood curve. Radiology 188:131–136CrossRefPubMedGoogle Scholar
  8. 8.
    Fung EK, Carson RE (2013) Cerebral blood flow with [15O]water PET studies using an image-derived input function and MR-defined carotid centerlines. Phys Med Biol 58:1903–1923CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Su Y, Arbelaez AM, Benzinger TL et al (2013) Noninvasive estimation of the arterial input function in positron emission tomography imaging of cerebral blood flow. J Cereb Blood Flow Metab 33:115–121CrossRefPubMedGoogle Scholar
  10. 10.
    Zanotti-Fregonara P, Chen K, Liow JS et al (2011) Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab 31:1986–1998CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Sanabria-Bohórquez SM, Labar D, Levêque P et al (2000) [11 C] Flumazenil metabolite measurement in plasma is not necessary for accurate brain benzodiazepine receptor quantification. Eur J Nucl Med 27:1674–1683CrossRefPubMedGoogle Scholar
  12. 12.
    Guo H, Renaut RA, Chen K (2007) An input function estimation method for FDG-PET human brain studies. Nucl Med Biol 34:483–492CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Riabkov DY, Di Bella EV (2002) Estimation of kinetic parameters without input functions: analysis of three methods for multichannel blind identification. IEEE Trans Biomed Eng 49:1318–1327CrossRefPubMedGoogle Scholar
  14. 14.
    Wong KPMS, Dagan F, Fulham MJ (2002) Estimation of input function and kinetic parameters using simulated annealing application in a flow model. IEEE Trans Nucl Sci 49:707–713CrossRefGoogle Scholar
  15. 15.
    Mikhno A, Zanderigo F, Todd Ogden R et al (2015) Toward noninvasive quantification of brain radioligand binding by combining electronic health records and dynamic PET imaging data. IEEE J Biomed Health 19:1271–1282CrossRefGoogle Scholar
  16. 16.
    Zanderigo F, Ogden RT, Parsey RV (2015) Noninvasive blood-free full quantification of positron emission tomography radioligand binding. J Cereb Blood Flow Metab 35:148–156CrossRefPubMedGoogle Scholar
  17. 17.
    Schain M, Zanderigo F, Mann JJ, Ogden RT (2017) Estimation of the binding potential BPND without a reference region or blood samples for brain PET studies. NeuroImage 146:121–131CrossRefPubMedGoogle Scholar
  18. 18.
    Roccia E, Mikhno A, Zanderigo F, et al. (2015) Non-invasive quantification of brain [(18)F]-FDG uptake by combining medical health records and dynamic PET imaging data. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2015:2243–2246Google Scholar
  19. 19.
    Wakita K, Imahori Y, Ido T et al (2000) Simplification for measuring input function of FDG PET: investigation of 1-point blood sampling method. J Nucl Med 41:1484–1490PubMedPubMedCentralGoogle Scholar
  20. 20.
    Wong DF, Wagner HN Jr, Tune LE et al (1986) Positron emission tomography reveals elevated D2 dopamine receptors in drug-naive schizophrenics. Science 234:1558–1563CrossRefPubMedGoogle Scholar
  21. 21.
    Milak MS, DeLorenzo C, Zanderigo F et al (2010) In vivo quantification of human serotonin 1A receptor using 11C-CUMI-101, an agonist PET radiotracer. J Nucl Med 51:1892–1900CrossRefPubMedGoogle Scholar
  22. 22.
    DeLorenzo C, Kumar JS, Mann JJ, Parsey RV (2011) In vivo variation in metabotropic glutamate receptor subtype 5 binding using positron emission tomography and [11C]ABP688. J Cereb Blood Flow Metab 31:2169–2180CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Wu S, Ogden RT, Mann JJ, Parsey RV (2007) Optimal metabolite curve fitting for kinetic modeling of 11C-WAY-100635. J Nucl Med 48:926–931CrossRefPubMedGoogle Scholar
  24. 24.
    Parsey RV, Ojha A, Ogden RT et al (2006) Metabolite considerations in the in vivo quantification of serotonin transporters using 11C-DASB and PET in humans. J Nucl Med 47:1796–1802PubMedGoogle Scholar
  25. 25.
    Zanderigo F, Ogden RT, Mann JJ, Parsey RV (2010) A voxel-based clustering approach for the automatic selection of testing regions in the simultaneous estimation of input functions in PET [abstract]. 52: S176PGoogle Scholar
  26. 26.
    Ogden RT (2003) Estimation of kinetic parameters in graphical analysis of PET imaging data. Stat Med 22:3557–3568CrossRefPubMedGoogle Scholar
  27. 27.
    Treyer V, Streffer J, Wyss MT et al (2007) Evaluation of the metabotropic glutamate receptor subtype 5 using PET and 11C-ABP688: assessment of methods. J Nucl Med 48:1207–1215CrossRefPubMedGoogle Scholar
  28. 28.
    DeLorenzo C, Milak MS, Brennan KG et al (2011) In vivo positron emission tomography imaging with [(1)(1)C]ABP688: binding variability and specificity for the metabotropic glutamate receptor subtype 5 in baboons. Eur J Nucl Med Mol Imaging 38:1083–1094CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Milak MS, Severance AJ, Ogden RT et al (2008) Modeling considerations for 11C-CUMI-101, an agonist radiotracer for imaging serotonin 1A receptor in vivo with PET. J Nucl Med 49:587–596CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci:54–75Google Scholar
  31. 31.
    Ogden RT, Ojha A, Erlandsson K et al (2007) In vivo quantification of serotonin transporters using [(11)C]DASB and positron emission tomography in humans: modeling considerations. J Cereb Blood Flow Metab 27:205–217CrossRefPubMedGoogle Scholar
  32. 32.
    Malatesha G, Singh NK, Bharija A et al (2007) Comparison of arterial and venous pH, bicarbonate, PCO2 and PO2 in initial emergency department assessment. Emerg Med J 24:569–571CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Parsey RV, Arango V, Olvet DM et al (2005) Regional heterogeneity of 5-HT1A receptors in human cerebellum as assessed by positron emission tomography. J Cereb Blood Flow Metab 25:785–793CrossRefPubMedGoogle Scholar
  34. 34.
    Turkheimer FE, Selvaraj S, Hinz R et al (2012) Quantification of ligand PET studies using a reference region with a displaceable fraction: application to occupancy studies with [(11)C]-DASB as an example. J Cereb Blood Flow Metab 32:70–80CrossRefPubMedGoogle Scholar
  35. 35.
    Salinas CA, Searle GE, Gunn RN (2015) The simplified reference tissue model: model assumption violations and their impact on binding potential. J Cereb Blood Flow Metab 35:304–311CrossRefPubMedGoogle Scholar
  36. 36.
    Todd Ogden R, Zanderigo F, Parsey RV (2015) Estimation of in vivo nonspecific binding in positron emission tomography studies without requiring a reference region. NeuroImage 108:234–242CrossRefPubMedGoogle Scholar
  37. 37.
    Slifstein M, Laruelle M (2001) Models and methods for derivation of in vivo neuroreceptor parameters with PET and SPECT reversible radiotracers. Nucl Med Biol 28:595–608CrossRefPubMedGoogle Scholar

Copyright information

© World Molecular Imaging Society 2018

Authors and Affiliations

  • Elizabeth A. Bartlett
    • 1
    Email author
  • Mala Ananth
    • 2
  • Samantha Rossano
    • 3
  • Mengru Zhang
    • 4
  • Jie Yang
    • 5
  • Shu-fei Lin
    • 6
  • Nabeel Nabulsi
    • 6
  • Yiyun Huang
    • 6
  • Francesca Zanderigo
    • 7
    • 8
  • Ramin V. Parsey
    • 1
    • 9
  • Christine DeLorenzo
    • 1
    • 9
  1. 1.Department of Biomedical EngineeringStony Brook UniversityStony BrookUSA
  2. 2.Department of NeuroscienceStony Brook UniversityStony BrookUSA
  3. 3.Department of Biomedical EngineeringYale UniversityNew HavenUSA
  4. 4.Department of Applied Mathematics and StatisticsStony Brook UniversityStony BrookUSA
  5. 5.Department of Family, Population, and Preventive MedicineStony Brook UniversityStony BrookUSA
  6. 6.Department of Radiology & Biomedical ImagingYale UniversityNew HavenUSA
  7. 7.Department of PsychiatryColumbia UniversityNew YorkUSA
  8. 8.Molecular Imaging and Neuropathology DivisionNew York State Psychiatric InstituteNew YorkUSA
  9. 9.Department of PsychiatryStony Brook UniversityStony BrookUSA

Personalised recommendations