Journal of Digital Imaging

, Volume 26, Issue 3, pp 447–456 | Cite as

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

  • A. Teymurazyan
  • T. Riauka
  • H.-S. Jans
  • D. Robinson
Article

Abstract

Noise levels observed in positron emission tomography (PET) images complicate their geometric interpretation. Post-processing techniques aimed at noise reduction may be employed to overcome this problem. The detailed characteristics of the noise affecting PET images are, however, often not well known. Typically, it is assumed that overall the noise may be characterized as Gaussian. Other PET-imaging-related studies have been specifically aimed at the reduction of noise represented by a Poisson or mixed Poisson + Gaussian model. The effectiveness of any approach to noise reduction greatly depends on a proper quantification of the characteristics of the noise present. This work examines the statistical properties of noise in PET images acquired with a GEMINI PET/CT scanner. Noise measurements have been performed with a cylindrical phantom injected with 11C and well mixed to provide a uniform activity distribution. Images were acquired using standard clinical protocols and reconstructed with filtered-backprojection (FBP) and row-action maximum likelihood algorithm (RAMLA). Statistical properties of the acquired data were evaluated and compared to five noise models (Poisson, normal, negative binomial, log-normal, and gamma). Histograms of the experimental data were used to calculate cumulative distribution functions and produce maximum likelihood estimates for the parameters of the model distributions. Results obtained confirm the poor representation of both RAMLA- and FBP-reconstructed PET data by the Poisson distribution. We demonstrate that the noise in RAMLA-reconstructed PET images is very well characterized by gamma distribution followed closely by normal distribution, while FBP produces comparable conformity with both normal and gamma statistics.

Keywords

Image processing Positron emission tomography (PET) Image denoising Nuclear medicine 

References

  1. 1.
    Caldwell CB, et al: Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: The impact of 18FDG-hybrid PET fusion. Int J Radiat Oncol Biol Phys 51(4):923–931, 2001PubMedCrossRefGoogle Scholar
  2. 2.
    Sailer SL, et al: Improving treatment planning accuracy through multimodality imaging. Int J Radiat Oncol Biol Phys 35(1):117–124, 1996PubMedCrossRefGoogle Scholar
  3. 3.
    Bar-Shalom R, et al: Clinical performance of PET/CT in evaluation of cancer: Additional value for diagnostic imaging and patient management. J Nucl Med 44(8):1200–1209, 2003PubMedGoogle Scholar
  4. 4.
    Bradley JD, et al: Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 45(Suppl 1):96S–101S, 2004PubMedGoogle Scholar
  5. 5.
    Drever LA: Positron emission tomography target volume delineation xiv + 134. Thesis, University of Alberta, 2005Google Scholar
  6. 6.
    Pieterman RM, et al: Preoperative staging of non-small-cell lung cancer with positron-emission tomography. N Engl J Med 343(4):254–261, 2000PubMedCrossRefGoogle Scholar
  7. 7.
    Kubota K, et al: Differential diagnosis of lung tumor with positron emission tomography: A prospective study. J Nucl Med 31(12):1927–1932, 1990PubMedGoogle Scholar
  8. 8.
    Weber W, et al: Assessment of pulmonary lesions with 18F-fluorodeoxyglucose positron imaging using coincidence mode gamma cameras. J Nucl Med 40(4):574–578, 1999PubMedGoogle Scholar
  9. 9.
    Vardi Y, Shepp LA, Kaufman L: A statistical model for positron emission tomography. J Amer Stat Assoc 80(389):8–20, 1985CrossRefGoogle Scholar
  10. 10.
    Tsui BM, et al: Analysis of recorded image noise in nuclear medicine. Phys Med Biol 26(5):883–902, 1981PubMedCrossRefGoogle Scholar
  11. 11.
    Rzeszotarski MS: Counting statistics. Radiographics 19(3):765–782, 1999PubMedGoogle Scholar
  12. 12.
    Rowe RW, Dai S: A pseudo-Poisson noise model for simulation of positron emission tomographic projection data. Med Phys 19(4):1113–1119, 1992PubMedCrossRefGoogle Scholar
  13. 13.
    Lange K, Carson R: EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr 8(2):306–316, 1984PubMedGoogle Scholar
  14. 14.
    Shepp LA, Vardi Y: Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imag 1(2):113–122, 1982CrossRefGoogle Scholar
  15. 15.
    Shepp LA, Logan BF: Fourier reconstruction of a head section. IEEE Trans Nucl Sci NS21(3):21–43, 1974Google Scholar
  16. 16.
    Kadrmas DJ: LOR-OSEM: Statistical PET reconstruction from raw line-of-response histograms. Phys Med Biol 49(20):4731–4744, 2004PubMedCrossRefGoogle Scholar
  17. 17.
    Razifar P: Novel approaches for application of principal component analysis on dynamic pet images for improvement of image quality and clinical diagnosis. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, x + 89, 2005Google Scholar
  18. 18.
    Wahl RL: In: Wahl RL Ed. Principles and Practice of Positron Emission Tomography. Lippincott Williams & Wilkins, Philadelphia, 2002, p 442Google Scholar
  19. 19.
    Green GC: Wavelet-based denoising of cardiac PET data xiv + 135. Dissertation, Carleton University, 2005Google Scholar
  20. 20.
    Ollinger JM, Fessler JA: Positron-emission tomography. EEE Signal Process Mag 14(1):43–55, 1997CrossRefGoogle Scholar
  21. 21.
    Coxson PG, Huesman RH, Borland L: Consequences of using a simplified kinetic model for dynamic PET data. J Nucl Med 38(4):660–667, 1997PubMedGoogle Scholar
  22. 22.
    Slifstein M, Mawlawi OR, Laruelle M: Chapter 11 (816): Partial volume effect correction: Methodological considerations. In: Gjedde A, Hansen SB, Knudsen GM, Paulson OB Eds. Physiological Imaging of the Brain with PET. Academic, San Diego, 2000, p 413Google Scholar
  23. 23.
    Rodrigues I, Sanches J, Bioucas-Dias J: Denoising of medical images corrupted by poisson noise. 15th IEEE International Conference on Image Processing 1–5(ICIP 2008):1756–1759, 2008Google Scholar
  24. 24.
    Hannequin P, Mas J: Statistical and heuristic image noise extraction (SHINE): A new method for processing Poisson noise in scintigraphic images. Phys Med Biol 47(24):4329–4344, 2002PubMedCrossRefGoogle Scholar
  25. 25.
    Němeček P: Filtrace šumu ve scintigrafických snímcích metodou založenou na Correspondence Analysis. v + 47, 2006Google Scholar
  26. 26.
    Seret A, Vanhove C, Defrise M: Resolution improvement and noise reduction in human pinhole SPECT using a multi-ray approach and the SHINE method. Nuklearmedizin 48(4):159–165, 2009PubMedGoogle Scholar
  27. 27.
    Budinger TF, et al: Quantitative potentials of dynamic emission computed tomography. J Nucl Med 19(3):309–315, 1978PubMedGoogle Scholar
  28. 28.
    Browne J, de Pierro AB: A row-action alternative to the EM algorithm for maximizing likelihood in emission tomography. IEEE Trans Med Imag 15(5):687–699, 1996CrossRefGoogle Scholar
  29. 29.
    Mandelkern MA: Nuclear techniques for medical imaging: Positron emission tomography. Annu Rev Nucl Part Sci 45:205–254, 1995CrossRefGoogle Scholar
  30. 30.
    Wilson DW, Tsui BMW: Noise properties of filtered-backprojection and ML-EM reconstructed emission tomographic images. IEEE Trans Nucl Sci 40(4):1198–1203, 1993CrossRefGoogle Scholar
  31. 31.
    Soares EJ, Byrne CL, Glick SJ: Noise characterization of block-iterative reconstruction algorithms: I. Theory. IEEE Trans Med Imaging 19(4):261–270, 2000PubMedCrossRefGoogle Scholar
  32. 32.
    Tanaka E, Kudo H: Subset-dependent relaxation in block-iterative algorithms for image reconstruction in emission tomography. Phys Med Biol 48(10):1405–1422, 2003PubMedCrossRefGoogle Scholar
  33. 33.
    Dempster AP, Laird NM, Rubin DB: Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B Meth 39(1):1–38, 1977Google Scholar
  34. 34.
    Hudson HM, Larkin RS: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 13(4):601–609, 1994PubMedCrossRefGoogle Scholar
  35. 35.
    Gonzalez RC, Woods RE: Digital Image Processing, 3rd edition. Pearson Prentice Hall, Upper Saddle River, 2008, p 954Google Scholar
  36. 36.
    NIST/SEMATECH: e-handbook of statistical methods. 2006(07/05/2006), 2010Google Scholar
  37. 37.
    Hilbe J: Negative Binomial Regression. Cambridge University Press, Cambridge, 2007, p 251CrossRefGoogle Scholar
  38. 38.
    Barrett HH, Wilson DW, Tsui BM: Noise properties of the EM algorithm: I. Theory. Phys Med Biol 39(5):833–846, 1994PubMedCrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2012

Authors and Affiliations

  • A. Teymurazyan
    • 1
    • 2
    • 4
  • T. Riauka
    • 2
    • 3
  • H.-S. Jans
    • 2
    • 3
  • D. Robinson
    • 1
    • 2
    • 3
  1. 1.Department of PhysicsUniversity of AlbertaEdmontonCanada
  2. 2.Department of Oncology, Medical Physics DivisionUniversity of AlbertaEdmontonCanada
  3. 3.Department of Medical PhysicsCross Cancer InstituteEdmontonCanada
  4. 4.Department of PhysicsUniversity of AlbertaEdmontonCanada

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