Advertisement

Automatic delineation algorithm of reference region for amyloid imaging based on kinetics

  • Takahiro Yamada
  • Shogo Watanabe
  • Takashi Nagaoka
  • Mitsutaka Nemoto
  • Kohei Hanaoka
  • Hayato Kaida
  • Kazunari Ishii
  • Yuichi KimuraEmail author
Original Article
  • 51 Downloads

Abstract

Objective

This study aims to develop an algorithm named AutoRef to delineate a reference region for quantitative PET amyloid imaging.

Methods

AutoRef sets the reference region automatically using a distinguishing feature in the kinetics of reference region. This is reflected in the shapes of the tissue time activity curve. A statistical shape recognition algorithm of the gaussian mixture model is applied with considering spatial and temporal information on a reference region. We evaluate the BPND with manually set reference region and AutoRef using 86 cases (43 positive cases, 10 equivocal cases, and 33 negative cases) of dynamically scanned 11C-Pittsburgh Compound-B.

Results

From the Bland–Altman plot, the difference between two BPND is 0.099 ± 0.21 as standard deviation, and no significant systematic error is observed between the BPND with AutoRef and with manual definition of a reference region. Although a proportional error is detected, it is smaller than the 95% limits of agreement. Therefore, the proportional error is negligibly small.

Conclusions

AutoRef presents the same performance as the manual definition of the reference region. Further, since AutoRef is more algorithmic than the ordinary manual definition of the reference region, there are few operator-oriented uncertainties in AutoRef. We thus conclude that AutoRef can be applied as an automatic delineating algorithm for the reference region in amyloid imaging.

Keywords

Amyloid imaging Reference region Kinetic analysis 

Notes

Acknowledgements

I express my gratitude to Dr. Chisa Hosokawa for her enthusiastic guidance from the beginning. This work is supported by MEXT KAKENHI Grant No. 15H01131 and Kindai University Research Grants: KD201805 from 2015 to 2017 and KD1804 from 2018.

Compliance with ethical standards

Conflict of interest

No potential conflict of interests was disclosed.

References

  1. 1.
    Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9:119–28.CrossRefGoogle Scholar
  2. 2.
    Julie CP, William EK, Brian JL, Xueling L, Jessica AH, Scott KZ, 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
  3. 3.
    Robert BI, Vincent JC, Jacques D, Fujita M, Albert G, Roger NG, et al. Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab. 2007;27:1533–9.CrossRefGoogle Scholar
  4. 4.
    Pasha R, Henry E, Anna R, Sergio E, Anders W, Bengt L. An automated method for delineating a reference region using masked volumewise principal-component analysis in 11C-PiB PET. J Nucl Med Technol. 2009;37:38–44.CrossRefGoogle Scholar
  5. 5.
    Geoffrey M, David P. Basic definition. Finite mixture models. New York: Wiley; 2000. p. 6–7.Google Scholar
  6. 6.
    Kimura Y, Yamada T, Hosokawa C, Okada S, Nagaoka T, Ishii K. Delineation algorithm on reference region for amyloid imaging using a time history of radioactivity: SNMMI 2016 annual meeting. J Nucl Med. 2016;57(2):311.Google Scholar
  7. 7.
    Yamada T, Kimura Y, Nagaoka T, Hosokawa C, Murakami T, Ishii K. Algorithm for automated delineation of reference regions using the pattern recognition scheme and kinetics of administered tracer—considering number of clustering—11th Human Amyloid Imaging Conference 2017, pp 36–37.Google Scholar
  8. 8.
    Geoffrey M, David P. Starting values for EM algorithm. Finite mixture models. New York: Wiley; 2000. p. 54–57.Google Scholar
  9. 9.
    Jean L, Joanna SF, Nora DV, Alfred PW, Stephen LD, David JS, et al. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-Methyl]-(-)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab. 1990;10:740–7.CrossRefGoogle Scholar
  10. 10.
    Jean L, Joanna SF, Nora DV, Gene-Jack W, Yu-Shin D, David LA. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab. 1996;16:834–40.CrossRefGoogle Scholar
  11. 11.
    Bart NMB, Rik O, Nelleke T, Maqsood Y, Jessica C, Foster D, Albert DW, et al. Longitudinal amyloid imaging using 11C-PiB: methodologic considerations. J Nucl Med. 2013;54:1570–6.CrossRefGoogle Scholar
  12. 12.
    Hosokawa C, Ishii K, Kimura Y, Hyodo T, Hosono M, Sakaguchi K, et al. Performance of 11C-Pittsburgh Compound B PET binding potential images in the detection of amyloid deposits on equivocal static images. J Nucl Med. 2015;56:1910–5.CrossRefGoogle Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2019

Authors and Affiliations

  1. 1.Department of Biological System Engineering, Graduate School of Biology-Oriented Science and TechnologyKindai UniversityKinokawa-shiJapan
  2. 2.Department of Human Health SciencesGraduate School of Medicine Kyoto UniversityKyotoJapan
  3. 3.Department of Biomedical Engineering, Faculty of Biology-Oriented Science and TechnologyKindai UniversityKinokawa-shiJapan
  4. 4.Division of Positron Emission Tomography, Institute of Advanced Clinical MedicineKindai UniversityOsakasayama-shiJapan
  5. 5.Department of Radiology, Faculty of MedicineKindai UniversityOsakasayama-shiJapan

Personalised recommendations