Correlation of early-phase 18F-florbetapir (AV-45/Amyvid) PET images to FDG images: preliminary studies

  • Ing-Tsung Hsiao
  • Chin-Chang Huang
  • Chia-Ju Hsieh
  • Wen-Chun Hsu
  • Shiaw-Pyng Wey
  • Tzu-Chen Yen
  • Mei-Ping KungEmail author
  • Kun-Ju LinEmail author
Original Article



18F-Florbetapir (AV-45/Amyvid) is a novel positron emission tomography (PET) tracer for imaging plaque pathology in Alzheimer’s disease (AD), while PET images of fluorodeoxyglucose (FDG) for cerebral glucose metabolism can provide complementary information to amyloid plaque images for diagnosis of AD. The goal of this preliminary study was to investigate the perfusion-like property of relative cerebral blood flow estimates (R1) and summed early-phase AV-45 images [perfusion AV-45 (pAV-45)] and optimize the early time frame for pAV-45.


Dynamic AV-45 PET scans (0–180 min) were performed in seven subjects. pAV-45, late-phase AV-45, and FDG images were spatially normalized to the Montreal Neurological Institute template aided by individual MRI images, and the corresponding standardized uptake value ratio (SUVR) was computed. The R1 images were derived from a simplified reference tissue model. Correlations between regional and voxelwise R1 and the corresponding FDG images were calculated. An optimization of time frames of pAV-45 was conducted in terms of correlation to FDG images. The optimal early time frame was validated in a separate cohort.


The regional distribution in the R1 images correlated well (R = 0.91) to that of the FDG within subjects. Consistently high correlation was noted across a long range of time frames. The maximal correlation of pAV-45 to FDG SUVR of R = 0.95 was observed at the time frame of 1–6 min, while the peak correlation of R = 0.99 happened at 0–2 min between pAV-45 and R1. A similar result was achieved in the validation cohort.


Preliminary results showed that the distribution patterns of R1 and pAV-45 images are highly correlated with normalized FDG images, and the initial 5-min early time frame of 1–6 min is potentially useful in providing complementary FDG-like information to the amyloid plaque density by late-phase AV-45 images.


Amyloid imaging Early time scan PET Relative cerebral blood flow estimates Alzheimer’s disease 



We thank Avid Radiopharmaceuticals Inc. (Philadelphia, PA, USA) for providing the precursor for the preparation of AV-45. This study was carried out with financial support from the National Science Council, Taiwan (Grant NSC-98-2314-B-182-034-MY2, NSC 100-2314-B-182-038 and 99-2314-B-182A-067-MY2), and grants from Research Fund of Chang Gung Memorial Hospital (CMRPD1A0311).

Conflicts of interest



  1. 1.
    Ikonomovic M, Klunk W, Abrahamson E, Mathis C, Price J, Tsopelas N, et al. Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain 2008;131:1630–45.PubMedCrossRefGoogle Scholar
  2. 2.
    Kemppainen NM, Aalto S, Wilson IA, Någren K, Helin S, Brück A, et al. Voxel-based analysis of PET amyloid ligand [11C]PIB uptake in Alzheimer disease. Neurology 2006;67:1575–80.PubMedCrossRefGoogle Scholar
  3. 3.
    Fripp J, Bourgeat P, Acosta O, Raniga P, Modat M, Pike K, et al. Appearance modeling of 11C PiB PET images: characterizing amyloid deposition in Alzheimer’s disease, mild cognitive impairment and healthy aging. Neuroimage 2008;43:430–9.PubMedCrossRefGoogle Scholar
  4. 4.
    Edison P, Archer HA, Hinz R, Hammers A, Pavese N, Tai YF, et al. Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology 2007;68:501–8.PubMedCrossRefGoogle Scholar
  5. 5.
    Verhoeff N, Wilson A, Takeshita S, Trop L, Hussey D, Singh K, et al. In-vivo imaging of Alzheimer disease beta-amyloid with [11C]SB-13 PET. Am J Geriatr Psychiatry 2004;12:584–95.PubMedGoogle Scholar
  6. 6.
    Choi SR, Golding G, Zhuang Z, Zhang W, Lim N, Hefti F, et al. Preclinical properties of 18F-AV-45: a PET agent for Abeta plaques in the brain. J Nucl Med 2009;50:1887–94.PubMedCrossRefGoogle Scholar
  7. 7.
    Lowe VJ, Kemp BJ, Jack Jr CR, Senjem M, Weigand S, Shiung M, et al. Comparison of 18F-FDG and PiB PET in cognitive impairment. J Nucl Med 2009;50:878–86.PubMedCrossRefGoogle Scholar
  8. 8.
    Zhou Y, Resnick S, Ye W, Fan H, Holt D, Klunk W, et al. Using a reference tissue model with spatial constraint to quantify [11C]Pittsburgh compound B PET for early diagnosis of Alzheimer’s disease. Neuroimage 2007;36:298–312.PubMedCrossRefGoogle Scholar
  9. 9.
    Ng S, Villemagne V, Berlangieri S, Lee S, Cherk M, Gong S, et al. Visual assessment versus quantitative assessment of 11C-PIB PET and 18F-FDG PET for detection of Alzheimer’s disease. J Nucl Med 2007;48:547–52.PubMedCrossRefGoogle Scholar
  10. 10.
    Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med 2008;49:390–8.PubMedCrossRefGoogle Scholar
  11. 11.
    Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry 2002;159:738–45.PubMedCrossRefGoogle Scholar
  12. 12.
    Treyer V, Streffer J, Wyss MT, Bettio A, Ametamey SM, Fischer U, et al. Evaluation of the metabotropic glutamate receptor subtype 5 using PET and 11C-ABP688: assessment of methods. J Nucl Med 2007;48:1207–15.PubMedCrossRefGoogle Scholar
  13. 13.
    Koeppe RA, Gilman S, Joshi A, Liu S, Little R, Junck L, et al. 11C-DTBZ and 18F-FDG PET measures in differentiating dementias. J Nucl Med 2005;46:936–44.PubMedGoogle Scholar
  14. 14.
    Murase K, Tanada S, Fujita H, Sakaki S, Hamamoto K. Kinetic behavior of technetium-99m-HMPAO in the human brain and quantification of cerebral blood flow using dynamic SPECT. J Nucl Med 1992;33:135–43.PubMedGoogle Scholar
  15. 15.
    Gur RC, Ragland JD, Reivich M, Greenberg JH, Alavi A, Gur RE. Regional differences in the coupling between resting cerebral blood flow and metabolism may indicate action preparedness as a default state. Cereb Cortex 2009;19:375–82.PubMedCrossRefGoogle Scholar
  16. 16.
    Wong CO, Thie J, Gaskill M, Ponto R, Hill J, Tian H, et al. A statistical investigation of normal regional intra-subject heterogeneity of brain metabolism and perfusion by F-18 FDG and O-15 H2O PET imaging. BMC Nucl Med 2006;6:4.PubMedCrossRefGoogle Scholar
  17. 17.
    Meyer PT, Hellwig S, Amtage F, Rottenburger C, Sahm U, Reuland P, et al. Dual-biomarker imaging of regional cerebral amyloid load and neuronal activity in dementia with PET and 11C-labeled Pittsburgh compound B. J Nucl Med 2011;52:393–400.PubMedCrossRefGoogle Scholar
  18. 18.
    Rostomian AH, Madison C, Rabinovici GD, Jagust WJ. Early 11C-PIB frames and 18F-FDG PET measures are comparable: a study validated in a cohort of AD and FTLD patients. J Nucl Med 2011;52:173–9.PubMedGoogle Scholar
  19. 19.
    Yao CH, Lin KJ, Weng CC, Hsiao IT, Ting YS, Yen TC, et al. GMP-compliant automated synthesis of [(18)F] AV-45 (Florbetapir F 18) for imaging beta-amyloid plaques in human brain. Appl Radiat Isot 2010;68:2293–7.PubMedCrossRefGoogle Scholar
  20. 20.
    Joshi A, Koeppe RA, Fessler JA. Reducing between scanner differences in multi-center PET studies. Neuroimage 2009;46:154–9.PubMedCrossRefGoogle Scholar
  21. 21.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273–89.PubMedCrossRefGoogle Scholar
  22. 22.
    Wu Y, Carson RE. Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J Cereb Blood Flow Metab 2002;22:1440–52.PubMedCrossRefGoogle Scholar
  23. 23.
    Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. Neuroimage 1996;4:153–8.PubMedCrossRefGoogle Scholar
  24. 24.
    Lin KJ, Hsu WC, Hsiao IT, Wey SP, Jin LW, Skovronsky D, et al. Whole-body biodistribution and brain PET imaging with [18F]AV-45, a novel amyloid imaging agent–a pilot study. Nucl Med Biol 2010;37:497–508.PubMedCrossRefGoogle Scholar
  25. 25.
    Clark CM, Schneider JA, Bedell BJ, Beach TG, Bilker WB, Mintun MA, et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA 2011;305:275–83.PubMedCrossRefGoogle Scholar
  26. 26.
    Nie NH, Bent DH, Hull CH. SPSS: Statistical Package for the Social Sciences. New York: McGraw-Hill; 1975.Google Scholar
  27. 27.
    Reivich M. Blood flow metabolism couple in brain. Res Publ Assoc Res Nerv Ment Dis 1974;53:125–40.PubMedGoogle Scholar
  28. 28.
    Powers WJ, Perlmutter JS, Videen TO, Herscovitch P, Griffeth LK, Royal HD, et al. Blinded clinical evaluation of positron emission tomography for diagnosis of probable Alzheimer’s disease. Neurology 1992;42:765–70.PubMedGoogle Scholar
  29. 29.
    Blomquist G, Engler H, Nordberg A, Ringheim A, Wall A, Forsberg A, et al. Unidirectional influx and net accumulation of PIB. Open Neuroimag J 2008;2:114–25.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Ing-Tsung Hsiao
    • 1
    • 2
  • Chin-Chang Huang
    • 3
  • Chia-Ju Hsieh
    • 1
    • 2
  • Wen-Chun Hsu
    • 3
  • Shiaw-Pyng Wey
    • 1
    • 2
  • Tzu-Chen Yen
    • 1
  • Mei-Ping Kung
    • 1
    • 2
    • 4
    Email author
  • Kun-Ju Lin
    • 1
    • 2
    Email author
  1. 1.Department of Nuclear Medicine and Molecular Imaging CenterChang Gung Memorial HospitalTaipeiTaiwan
  2. 2.Healthy Aging Research Center and Department of Medical Imaging and Radiological SciencesChang Gung UniversityTaipeiTaiwan
  3. 3.Department of NeurologyChang Gung Memorial HospitalTaipeiTaiwan
  4. 4.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations