Skip to main content
SpringerLink
Log in

Bootstrap methods for estimating PET image noise: experimental validation and an application to evaluation of image reconstruction algorithms

  • Technical note
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

Accurate and validated methods for estimating regional PET image noise are helpful for optimizing image processing. The bootstrap is a data-based simulation method for statistical inference, which can be used to estimate the PET image noise without repeated measurements. The aim of this study was to experimentally validate bootstrap-based methods as a tool for estimating PET image noise and demonstrate its usefulness for evaluating image reconstruction algorithms.

Methods

Two bootstrap-based method, the list-mode data bootstrap (LMBS) and the sinogram bootstrap (SNBS), were implemented on a clinical PET scanner. A uniform cylindrical phantom filled with 18F solution was scanned using list-mode acquisition. A reference standard deviation (SD) map was calculated from 60 statistically independent measured list-mode data. Using one of the 60 list-mode data, 60 bootstrap replicates were generated and used to calculate bootstrap SD maps. Brain 18F-FDG data from a healthy volunteer were also processed as an example of the bootstrap application. Three reconstruction algorithms, FBP 2D and both 2D and 3D versions of dynamic row-action maximum likelihood algorithm (DRAMA), were assessed.

Results

For all the reconstruction algorithms used, the bootstrap SD maps agreed well with the reference SD map, confirming the validity of the bootstrap methods for assessing image noise. The two bootstrap methods were equivalent with respect to the performance of image noise estimation. The bootstrap analysis of the FDG data showed the better contrast–noise relation curve for DRAMA 3D compared to DRAMA 2D and FBP 2D.

Conclusions

The bootstrap methods provide the estimates of image noise for various reconstruction algorithms with reasonable accuracy, require only a single measurement, not repeated measures, and are, therefore, applicable for a human PET study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Dahlbom M. Estimation of image noise in PET using the bootstrap method. IEEE Trans Nucl Sci. 2002;49:2062–6.

    Article  Google Scholar 

  2. Alpert NM, Chesler DA, Correia JA, Ackerman RH, Chang JY, Finklestein S, et al. Estimation of the local statistical noise in emission computed tomography. IEEE Trans Med Imaging. 1982;1:142–6.

    Article  CAS  PubMed  Google Scholar 

  3. Carson RE, Yan Y, Daube-Witherspoon ME, Freedman N, Bacharach SL, Herscovitch P. An approximation formula for the variance of PET region-of-interest values. IEEE Trans Med Imaging. 1993;12:240–50.

    Article  CAS  PubMed  Google Scholar 

  4. Barrett HH, Wilson DW, Tsui BM. Noise properties of the EM algorithm: I. Theory. Phys Med Biol. 1994;39:833–46.

    Article  CAS  PubMed  Google Scholar 

  5. Soares EJ, Byrne CL, Glick SJ. Noise characterization of block-iterative reconstruction algorithms: I. Theory. IEEE Trans Med Imaging. 2000;19:261–70.

    Article  CAS  PubMed  Google Scholar 

  6. Qi J, Leahy RM. Resolution and noise properties of MAP reconstruction for fully 3-D PET. IEEE Trans Med Imaging. 2000;19:493–506. doi:10.1109/42.870259.

    Article  CAS  PubMed  Google Scholar 

  7. Nickerson LD, Narayana S, Lancaster JL, Fox PT, Gao JH. Estimation of the local statistical noise in positron emission tomography revisited: practical implementation. Neuroimage. 2003;19:442–56.

    Article  PubMed  Google Scholar 

  8. Haynor DR, Woods SD. Resampling estimates of precision in emission tomography. IEEE Trans Med Imaging. 1989;8:337–43.

    Article  CAS  PubMed  Google Scholar 

  9. Buvat I. A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images. Phys Med Biol. 2002;47:1761–75.

    Article  PubMed  Google Scholar 

  10. Lartizien C, Aubin JB, Buvat I. Comparison of bootstrap resampling methods for 3-D PET imaging. IEEE Trans Med Imaging. 2010;29:1442–54. doi:10.1109/TMI.2010.2048119.

    Article  CAS  PubMed  Google Scholar 

  11. Efron B, Tibshirani RJ. An introduction to the bootstrap. Boca Raton: Chapman & Hall/CRC; 1994.

    Google Scholar 

  12. Matsumoto K, Kitamura K, Mizuta T, Tanaka K, Yamamoto S, Sakamoto S, et al. Performance characteristics of a new 3-dimensional continuous-emission and spiral-transmission high-sensitivity and high-resolution PET camera evaluated with the NEMA NU 2-2001 standard. J Nucl Med. 2006;47:83–90.

    PubMed  Google Scholar 

  13. Ibaraki M, Miura S, Shimosegawa E, Sugawara S, Mizuta T, Ishikawa A, et al. Quantification of cerebral blood flow and oxygen metabolism with 3-dimensional PET and 15O: validation by comparison with 2-dimensional PET. J Nucl Med. 2008;49:50–9.

    Article  PubMed  Google Scholar 

  14. Ibaraki M, Sugawara S, Nakamura K, Kinoshita F, Kinoshita T. The effect of activity outside the field-of-view on image signal-to-noise ratio for 3D PET with (15)O. Phys Med Biol. 2011;56:3061–72. doi:10.1088/0031-9155/56/10/011 S0031-9155(11)76857-9[pii].

    Article  PubMed  Google Scholar 

  15. Tanaka E, Kudo H. Subset-dependent relaxation in block-iterative algorithms for image reconstruction in emission tomography. Phys Med Biol. 2003;48:1405–22.

    Article  PubMed  Google Scholar 

  16. Tanaka E, Kudo H. Optimal relaxation parameters of DRAMA (dynamic RAMLA) aiming at one-pass image reconstruction for 3D-PET. Phys Med Biol. 2010;55:2917–39. doi:10.1088/0031-9155/55/10/009.

    Article  PubMed  Google Scholar 

  17. Ibaraki M, Sato K, Mizuta T, Kitamura K, Miura S, Sugawara S, et al. Evaluation of dynamic row-action maximum likelihood algorithm reconstruction for quantitative 15O brain PET. Ann Nucl Med. 2009;23:627–38.

    Article  PubMed  Google Scholar 

  18. Defrise M, Kinahan PE, Townsend DW, Michel C, Sibomana M, Newport DF. Exact and approximate rebinning algorithms for 3-D PET data. IEEE Trans Med Imaging. 1997;16:145–58.

    Article  CAS  PubMed  Google Scholar 

  19. Browne J, de Pierro AB. A row-action alternative to the EM algorithm for maximizing likelihood in emission tomography. IEEE Trans Med Imaging. 1996;15:687–99.

    Article  CAS  PubMed  Google Scholar 

  20. Tong S, Alessio AM, Kinahan PE. Noise and signal properties in PSF-based fully 3D PET image reconstruction: an experimental evaluation. Phys Med Biol. 2010;55:1453–73. doi:10.1088/0031-9155/55/5/013.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Shidahara M, Ikoma Y, Kershaw J, Kimura Y, Naganawa M, Watabe H. PET kinetic analysis: wavelet denoising of dynamic PET data with application to parametric imaging. Ann Nucl Med. 2007;21:379–86.

    Article  PubMed  Google Scholar 

  22. Myers K, Barrett H, Borgstrom M, Patton D, Seeley G. Effect of noise correlation on detectability of disk signals in medical imaging. J Opt Soc Am A. 1985;2:1752.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We thank the staff of the Akita Research Institute of Brain and Blood Vessels, in particular Kaoru Sato and Tomomi Ohmura for performing the PET experiment. We also thank Tetsuro Mizuta of Shimadzu Corporation for helping to handle list-mode format data. This work was supported by MEXT KAKENHI Grant Number 18790923.

Author information

Authors and Affiliations

  1. Department of Radiology and Nuclear Medicine, Akita Research Institute of Brain and Blood Vessels, 6-10 Senshu-Kubota Machi, Akita, 010-0874, Japan

    Masanobu Ibaraki, Keisuke Matsubara, Kazuhiro Nakamura, Hiroshi Yamaguchi & Toshibumi Kinoshita

Authors
  1. Masanobu Ibaraki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keisuke Matsubara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kazuhiro Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiroshi Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Toshibumi Kinoshita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masanobu Ibaraki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ibaraki, M., Matsubara, K., Nakamura, K. et al. Bootstrap methods for estimating PET image noise: experimental validation and an application to evaluation of image reconstruction algorithms. Ann Nucl Med 28, 172–182 (2014). https://doi.org/10.1007/s12149-013-0782-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-013-0782-9

Keywords

Search

Navigation