Annals of Nuclear Medicine

, Volume 25, Issue 9, pp 643–649 | Cite as

Comparative evaluation of scatter correction in 3D PET using different scatter-level approximations

  • Irene Polycarpou
  • Kris Thielemans
  • Ravindra Manjeshwar
  • Pablo Aguiar
  • Paul K. Marsden
  • Charalampos Tsoumpas
Original article

Abstract

Objective

In 3D PET, scatter of the gamma photons is one of the most significant physical factors which degrades not only image quality but also quantification. The currently most used scatter estimation method is the analytic single scatter simulation (SSS) which usually accommodates for multiple scattering by scaling the single scatter estimation. However, it has not been clear yet how accurate this approximation is for cases where multiple scatter is significant, raising the question: “How important is correction for multiple scattered photons, and how accurately do we need to simulate all scattered events by appropriate scaling?” This study answers these questions and evaluates the accuracy of SSS implementation in the open-source library STIR.

Methods

Different scatter orders approximations are evaluated including different levels of scattering and different scaling approaches using Monte Carlo (i.e. SimSET) data. SimSET simulations of a large anthropomorphic phantom were reconstructed with iterative reconstruction algorithms. Images reconstructed with 3D filtered back-projection reprojection algorithm have been compared quantitatively in order to clarify the errors due to different scatter order approximations.

Results

Quantification in regions has improved by scatter correction. For example, in the heart the ideal value was 3, whereas before scatter correction the standard uptake value (SUV) was 4.0, after single scatter correction was 3.3 and after single and double scatter correction was 3.0. After correction by scaling single scatter with tail-fit, the SUV was 3.1, whereas with total-fit it was 3.0. Similarly, for the SSS correction methodology implemented in STIR using tail-fit the heart SUV was 3.1 whereas using total-fit it was 3.0.

Conclusions

The results demonstrate that correction for double scatter improves image contrast and therefore it is required for the accurate estimation of activity distribution in PET imaging. However, it has been also shown that scaling the single scatter distribution is a reasonable approximation to compensate for total scatter. Finally, scatter correction with STIR has shown excellent agreement with Monte Carlo simulations.

Keywords

Scatter Quantification Monte Carlo STIR 

Notes

Acknowledgments

The authors wish to express their gratitude to Dr Robert Harrison (University of Washington) for valuable advice on SimSET simulations.

References

  1. 1.
    Zaidi H. Comparative evaluation of scatter correction techniques in 3D positron emission tomography. Eur J Nucl Med Mol Imaging. 2000;27:1813–26.CrossRefGoogle Scholar
  2. 2.
    Zaidi H. Scatter modelling and correction strategies in fully 3-D PET. Nucl Med Commun. 2001;22:1181.PubMedCrossRefGoogle Scholar
  3. 3.
    Watson C, Newport D, Casey M. A single scatter simulation technique for scatter correction in 3D PET. 3D Image Reconstr Radiol Nucl Med. 1996;4:255–68.Google Scholar
  4. 4.
    Watson C, Newport D, Casey M, Beanlands RS, Schmand M, De Kemp RA. Evaluation of simulation-based scatter correction for 3-D PET cardiac imaging. IEEE Trans Nucl Sci. 1997;44:90–7.CrossRefGoogle Scholar
  5. 5.
    Ollinger JM. Model-based scatter correction for fully 3D PET. Phys Med Biol. 1996;41:153–76.PubMedCrossRefGoogle Scholar
  6. 6.
    Zaidi H, Koral K. Scatter modelling and compensation in emission tomography. Eur J Nucl Med Mol Imaging. 2004;31:761–82.PubMedCrossRefGoogle Scholar
  7. 7.
    Zaidi H, Montandon M. Scatter compensation techniques in PET. PET Clin. 2007;2:219–34.CrossRefGoogle Scholar
  8. 8.
    Accorsi R, Adam L, Werner M, Karp J. Optimization of a fully 3D single scatter simulation algorithm for 3D PET. Phys Med Biol. 2004;49:2577–98.PubMedCrossRefGoogle Scholar
  9. 9.
    Iatrou M, Manjeshwar RM, Ross SG, Thielemans K, Stearns CW. 3D implementation of scatter estimation in 3D PET. In: Proceedings of the 2006 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2006.Google Scholar
  10. 10.
    Poenisch F, Enghardt W, Lauckner K. Attenuation and scatter correction for in-beam positron emission tomography monitoring of tumour irradiations with heavy ions. Phys Med Biol. 2003;48:2419–36.CrossRefGoogle Scholar
  11. 11.
    Werling A, Bublitz O, Doll J, Adam LE, Brix G. Fast implementation of the single scatter simulation algorithm and its use in iterative image reconstruction of PET data. Phys Med Biol. 2002;47:2947–60.PubMedCrossRefGoogle Scholar
  12. 12.
    Watson CC. New, faster, image-based scatter correction for 3D PET. IEEE Trans Nucl Sci. 2000;47:1587–94.CrossRefGoogle Scholar
  13. 13.
    Thielemans K, Manjeshwar RM, Tsoumpas C, Jansen FP. A new algorithm for scaling of PET scatter estimates using all coincidence events. In: Proceedings of the 2007 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2007.Google Scholar
  14. 14.
    Thielemans K, Mustafovic S, Tsoumpas C. STIR: Software for Tomographic Image Reconstruction Release 2. In: Proceedings of the 2006 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2006.Google Scholar
  15. 15.
    Tsoumpas C, Aguiar P, Nikita KS, Ros D, Thielemans K. Evaluation of the single scatter simulation algorithm implemented in the STIR library. In: Proceedings of the 2004 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2004.Google Scholar
  16. 16.
    Goggin AS, Ollinger JM. A model for multiple scatters in fully 3D PET. In: Proceedings of the 1994 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 1994.Google Scholar
  17. 17.
    Adam L, Karp J, Brix G. Investigation of scattered radiation in 3D whole-body positron emission tomography using Monte Carlo simulations. Phys Med Biol. 1999;44:2879–95.PubMedCrossRefGoogle Scholar
  18. 18.
    Tsoumpas C, Aguiar P, Ros D, Dikaios N, Thielemans K. Scatter simulation including double scatter. In: Proceedings of the 2005 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2005.Google Scholar
  19. 19.
    Qian H, Manjeshwar RM, Thielemans K. A comparative study of multiple scatters in 3D PET. In: Proceedings of the 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2010.Google Scholar
  20. 20.
    Zubal IG, Harrell CR, Smith EO, Rattner Z, Gindi G, Hoffer PB. Computerized three-dimensional segmented human anatomy. Med Phys. 1994;21:299–302.PubMedCrossRefGoogle Scholar
  21. 21.
    Lewellen T, Harrison R, Vannoy S. The SIMSET program. In: Ljungberg M, Strand S-E, King M, editors. Monte Carlo calculations in nuclear medicine. London: IOP Publishers; 1998. p. 77–92.Google Scholar
  22. 22.
    Shao Y, Manjeshwar R, Jansen F, Kumar P. PSM: PET system modeling capable of generating images with clinically relevant count density. In: Proceedings of the 2003 IEEE Nuclear Science Symposium and Medical Imaging Conference. 2003.Google Scholar
  23. 23.
    Kinahan PE, Rogers JG. Analytic 3D image reconstruction using all detected events. IEEE Trans Nucl Sci. 1989;36:964–8.CrossRefGoogle Scholar
  24. 24.
    Loening A, Gambhir S. AMIDE: a free software tool for multimodality medical image analysis. Mol Imaging. 2003;2:131–7.PubMedCrossRefGoogle Scholar
  25. 25.
    Hogg D, Mustafovic S, Thielemans K, Spinks TJ. A study of bias for various iterative reconstruction methods in PET. In: Proceedings of the 2002 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2002.Google Scholar
  26. 26.
    Ahn S, Fessler JA. Statistical emission image reconstruction for randoms-precorrected PET scans using negative sinogram values. In: Proceedings of the 2003 IEEE Nuclear Science Symposium and Medical Imaging Conference Record. 2003.Google Scholar
  27. 27.
    Gao F, Yamada R, Watanabe M, Liu H-F. An effective scatter correction method based on single scatter simulation for a 3D whole-body PET scanner. Chin Phys B. 2009;18:3066.CrossRefGoogle Scholar
  28. 28.
    Ahasan MM, Parker DJ. Design and performance evaluation of a prototype large ring PET scanner. In: Ao SI, Gelman L, editors. Advances in electrical engineering and computational science, vol. 39. The Netherlands: Springer; 2009. p. 161–72.CrossRefGoogle Scholar
  29. 29.
    Cade S, Bousse A, Arridge S, Evans M, Hutton B. Estimating an attenuation map from measured scatter for 180o cardiac SPECT. In: Proceedings of the 2010 SNM Annual Meeting. 2010.Google Scholar
  30. 30.
    Markiewicz PJ, Tamal M, Julyan PJ, Hastings DL, Reader AJ. High accuracy multiple scatter modelling for 3D whole body PET. Phys Med Biol. 2007;52:829–47.PubMedCrossRefGoogle Scholar
  31. 31.
    Guerin B, El Fakhri G. Novel scatter compensation of list-mode PET data using spatial and energy dependent correction. IEEE Trans Med Imaging. 2011;30:759–73.PubMedCrossRefGoogle Scholar
  32. 32.
    Ferreira NC, Trébossen R, Lartizien C, Brulon V, Merceron P, Bendriem B. A hybrid scatter correction for 3D PET based on an estimation of the distribution of unscattered coincidences: implementation on the ECAT EXACT HR+. Phys Med Biol. 2002;47:1555–71.Google Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2011

Authors and Affiliations

  • Irene Polycarpou
    • 1
  • Kris Thielemans
    • 2
  • Ravindra Manjeshwar
    • 3
  • Pablo Aguiar
    • 4
  • Paul K. Marsden
    • 1
  • Charalampos Tsoumpas
    • 1
  1. 1.Division of Imaging Sciences and Biomedical Engineering, The Rayne Institute, 4th floor Lambeth WingSt. Thomas’ Hospital, King’s College LondonLondonUK
  2. 2.Hammersmith Imanet Ltd., General Electric HealthcareLondonUK
  3. 3.General Electric Global ResearchAlbanyUSA
  4. 4.GCA Molecular Imaging (IDIS), Fundación IDICHUSSantiago de CompostelaSpain

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