Advertisement

Radiological Physics and Technology

, Volume 11, Issue 4, pp 451–459 | Cite as

Investigation of the quantitative accuracy of low-dose amyloid and tau PET imaging

  • Ying-Hwey Nai
  • Shoichi Watanuki
  • Manabu Tashiro
  • Nobuyuki Okamura
  • Hiroshi Watabe
Article
  • 62 Downloads

Abstract

With the increasing incidence of dementia worldwide, the frequent use of amyloid and tau positron emission tomography imaging requires low-dose protocols for the differential diagnoses of various neurodegenerative diseases and the monitoring of disease progression. In this study, we investigated the feasibility to reduce the PET dose without a significant loss of quantitative accuracy in 3D dynamic row action maximum likelihood algorithm-reconstructed PET images using [11C]PIB and [18F]THK5351. Eighteen cognitively normal young controls, cognitively normal elderly controls, and patients with probable Alzheimer’s disease (n = 6 each), were included. Reduced doses were simulated by randomly sampling half and quarter of the full counts in list mode data for one independent realization at each simulated dose. Bias was evaluated between the reduced dose from the full dose of standardized uptake value ratio (SUVR), distribution volume ratio (DVR) from reference Logan, and non-displaceable binding potential (BPND) from simplified reference tissue model (SRTM). DVR yielded the least bias at low dose compared to SUVR and BPND, and thus, is highly recommended. The dose of [18F]THK5351 and [11C]PIB can be reduced to a quarter of the full dose using DVR for evaluation, whereas the dose can only be reduced to half and a quarter of the full dose for [18F]THK5351 and [11C]PIB using SUVR. BPND showed inconsistent trend and large bias at low dose. The feasibility of dose reduction was dependent on the selected parameters of interest, reconstruction algorithms, reference regions, and to a lesser degree by motion effects.

Keywords

Alzheimer’s disease Tau Amyloid Low dose Positron emission tomography 

Notes

Acknowledgements

This study was supported by Grants-in-Aid for Scientific Research (B) (No. 17H04118) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japanese Government.

Compliance with ethical standards

Conflict of interest

No potential conflicts of interest were disclosed.

Statement of human and animal rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of Tohoku University and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies performed with animals.

Informed consent

Written informed consent was obtained before enrolling the subjects in the study.

Supplementary material

12194_2018_485_MOESM1_ESM.docx (182 kb)
Supplementary material 1 (DOCX 182 KB)

References

  1. 1.
    Perrin RJ, Fagan AM, Holtzman DM. Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease. Nature. 2009;461:916–22.CrossRefGoogle Scholar
  2. 2.
    Villemagne VL, Fodero-Tavoletti MT, Masters CL, Rowe CC. Tau imaging: early progress and future directions. Lancet Neurol. 2015;14(1):114–24.CrossRefGoogle Scholar
  3. 3.
    Catafau AM, Bullich S, Amyloid. PET imaging: applications beyond Alzheimer’s disease. Clin Transl Image. 2015;3:39–55.CrossRefGoogle Scholar
  4. 4.
    Dorbala S, Blankstein R, Skali H, Park M-A, Fantony J, Mauceri C, et al. Approaches to reducing radiation dose from radionuclide myocardial perfusion imaging. J Nucl Med. 2015;Apr 1;56(4):592–9.CrossRefGoogle Scholar
  5. 5.
    Fällmar D, Lilja J, Kilander L, Danfors T, Lubberink M, Larsson E-M, et al. Validation of true low-dose 18F-FDG PET of the brain. Am J Nucl Med Mol Image. 2016;6(5):269.Google Scholar
  6. 6.
    Flavell RR, Naeger DM, Mari Aparici C, Hawkins RA, Pampaloni MH, Behr SC. Malignancies with low fluorodeoxyglucose uptake at PET/CT: pitfalls and prognostic importance: resident and fellow education feature. Radio Graph. 2016;36(1):293–4.Google Scholar
  7. 7.
    Zhang J, Yang R, Liu Z, Hou C, Zong W, Zhang A, et al. Optimized dose regimen for whole-body FDG-PET imaging. Eur J Nucl Med Mol Image Res. 2013;3:63.Google Scholar
  8. 8.
    Tanaka E, Kudo H. Optimal relaxation parameters of DRAMA (dynamic RAMLA) aiming at one-pass image reconstruction for 3D-PET. Phys Med Biol. 2010;55(10):2917–39.CrossRefGoogle Scholar
  9. 9.
    Logan DVR: Logan J, Fowler JS, Volkow ND, Wang G-J, Ding Y-S, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cerebral Blood Flow Metab. 1996;16(5):834–40.CrossRefGoogle Scholar
  10. 10.
    Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. Neuroimage. 1996;4(3):153–8.CrossRefGoogle Scholar
  11. 11.
    Ibaraki M, Matsubara K, Sato K, Mizuta T, Kinoshita T. Validation of a simplified scatter correction method for 3D brain PET with 15O. Ann Nucl Med. 2016;30(10):690–8.CrossRefGoogle Scholar
  12. 12.
    Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968–80.CrossRefGoogle Scholar
  13. 13.
    Price JC, et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab. 2005;25:1528–47.CrossRefGoogle Scholar
  14. 14.
    Landau SM, et al. Amyloid PET imaging in Alzheimer’s disease: a comparison of three radiotracers. Eur J Nucl Med Mol Image. 2014;41:1398–407.CrossRefGoogle Scholar
  15. 15.
    Chen K, et al. Improved power for characterizing longitudinal amyloid- PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. J Nucl Med. 2015;56:560–6.CrossRefGoogle Scholar
  16. 16.
    Reilhac A, Tomeï S, Buvat I, Michel C, Keheren F, Costes N. Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies. NeuroImage. 2008 Jan;39(1):359–68.CrossRefGoogle Scholar
  17. 17.
    Jucker M, Walker LC. Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature. 2013;501(7465):45–51.CrossRefGoogle Scholar

Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2018

Authors and Affiliations

  1. 1.Division of Radiation Informatics for Medical Imaging, Graduate School of Biomedical EngineeringTohoku UniversitySendaiJapan
  2. 2.Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope Center (CYRIC)Tohoku UniversitySendaiJapan
  3. 3.Division of Cyclotron Nuclear Medicine, Cyclotron and Radioisotope CenterTohoku UniversitySendaiJapan
  4. 4.Tohoku Medical and Pharmaceutical UniversitySendaiJapan

Personalised recommendations