A Probabilistic Patch Based Hybrid Framework for CT/PET Image Reconstruction

  • Shailendra Tiwari
  • Rajeev Srivastava
  • Arvind Kumar Tiwari
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


Statistical image reconstruction for computed tomography and positron emission tomography (CT/PET) play a significant role in the image quality by using spatial regularization that penalizes image intensity difference between neighboring pixels. The most commonly used quadratic membrane (QM) prior, which smooth’s both high frequency noise and edge details, tends to produce an unfavourable result while edge-preserving non-quadratic priors tend to produce blocky piecewise regions. However, these edge-preserving priors mostly depend on local smoothness or edges. It does not consider the basic fine structure information of the desired image, such as the gray levels, edge indicator, dominant direction and frequency. To address the aforementioned issues of the conventional regularizations/priors, this paper introduces and evaluates a hybrid approach to regularized ordered subset expectation maximization (OSEM) iterative reconstruction technique, which is an accelerated version of EM, with Poisson variability. Regularization is achieved by penalizing OSEM with probabilistic patch-based regularization (PPB) filter to form hybrid method (OSEM+PPB) for CT/PET image reconstruction that uses neighborhood patches instead of individual pixels in computing the non-quadratic penalty. The aim of this paper is to impose an effective edge preserving and noise removing framework to optimize the quality of CT/PET reconstructed images. A comparative analysis of the proposed model with some other existing standard methods in literature is presented both qualitatively and quantitatively using simulated test phantom and standard digital image. An experimental result indicates that the proposed method yields significantly improvements in quality of reconstructed images from the projection data. The obtained results justify the applicability of the proposed method.


Reconstruction algorithms Computed tomography (CT) Positron emission tomography (PET) Ordered subset expectation-maximization algorithms (OSEM) Probabilistic patch based prior (PPB) Acceleration techniques 


  1. 1.
    Zeng, G.L.: Comparison of a noise-weighted filtered backprojection algorithm with the Standard MLEM algorithm for poisson noise. J. Med. Technol. (2013)Google Scholar
  2. 2.
    Shepp, L.A., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imag. 1(2), 113 –122, Oct. 1982Google Scholar
  3. 3.
    Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imag. 13(4) (1994)Google Scholar
  4. 4.
    Ling, J., Bovik, A.C.: Smoothing low-SNR molecular image via anisotropic median-diffusion. IEEE Trans. Med. Imag. 21(4), 377–384 (2002)CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Chen, W.F., Feng, Y.Q., Feng, Q.J.: Convergent Bayesian reconstruction for PET using new MRF quadratic membrane-plate hybrid multi-order prior, lecture notes in computer science. Med. Imag. Aug. Real. (2006)Google Scholar
  6. 6.
    Lange, K.: Convergence of EM image reconstruction algorithms with Gibbs smoothness. IEEE Trans. Med. Imag. 9, 439 (1990)CrossRefGoogle Scholar
  7. 7.
    Denisova, N.V.: Bayesian reconstruction in SPECT with entropy prior and iterative statistical regularization. IEEE Trans. Nucl. Sci. (2004)Google Scholar
  8. 8.
    Chlewicki, W., Hermansin, F., Hansen, S.B.: Noise reduction and convergence of Bayesian algorithms with blobs based on the Huber function and median root prior. Phys. Med. Biol. (2004)Google Scholar
  9. 9.
    Panin, V.Y., Zeng, G.L., Gullberg, G.T.: Total variation regulated EM algorithm. IEEE Trans. Nucl. Sci. 46, 2202–2210 (1999)CrossRefGoogle Scholar
  10. 10.
    Sukovic, P., Clinthorne, N.H.: Penalized weighted least-squares image reconstruction in single and dual energy X-ray computed tomography. IEEE Trans. Med. Imaging 19(11), 1075–1081 (2000)CrossRefGoogle Scholar
  11. 11.
    Kazantsev, D., Arridge, S.R., Pedemonte, S., et al.: An anatomically driven anisotropic diffusion filtering method for 3D SPECT reconstruction. Phys. Med. Biol. (2012)Google Scholar
  12. 12.
    Wang, G., Qi, J.: Penalized likelihood PET image reconstruction using patch-based edgepreserving regularization. IEEE Trans. Med. Imag. 31(12), 2194–2204 (2012)CrossRefGoogle Scholar
  13. 13.
    Wernick, M.N., Aarsvold, J.N.: Emission Tomography: The Fundamentals of PET and SPECT. Academic Press (2004)Google Scholar
  14. 14.
    Srivastava, Rajeev, Srivastava, Subodh: Restoration of Poisson noise corrupted digital images with nonlinear PDE based filters along with the choice of regularization parameter estimation. Pattern Recogn. Lett. 34, 1175–1185 (2013)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Shailendra Tiwari
    • 1
  • Rajeev Srivastava
    • 1
  • Arvind Kumar Tiwari
    • 1
  1. 1.Department of Computer Science & EngineeringIndian Institute of Technology (Banaras Hindu University)VaranasiIndia

Personalised recommendations