Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2013, Special Issue (2013), Article ID 732178, 14 pages.

A Novel Adaptive Probabilistic Nonlinear Denoising Approach for Enhancing PET Data Sinogram

Musa Alrefaya and Hichem Sahli

Full-text: Open access

Abstract

We propose filtering the PET sinograms with a constraint curvature motion diffusion. The edge-stopping function is computed in terms of edge probability under the assumption of contamination by Poisson noise. We show that the Chi-square is the appropriate prior for finding the edge probability in the sinogram noise-free gradient. Since the sinogram noise is uncorrelated and follows a Poisson distribution, we then propose an adaptive probabilistic diffusivity function where the edge probability is computed at each pixel. The filter is applied on the 2D sinogram prereconstruction. The PET images are reconstructed using the Ordered Subset Expectation Maximization (OSEM). We demonstrate through simulations with images contaminated by Poisson noise that the performance of the proposed method substantially surpasses that of recently published methods, both visually and in terms of statistical measures.

Article information

Source
J. Appl. Math., Volume 2013, Special Issue (2013), Article ID 732178, 14 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394807820

Digital Object Identifier
doi:10.1155/2013/732178

Zentralblatt MATH identifier
1271.94004

Citation

Alrefaya, Musa; Sahli, Hichem. A Novel Adaptive Probabilistic Nonlinear Denoising Approach for Enhancing PET Data Sinogram. J. Appl. Math. 2013, Special Issue (2013), Article ID 732178, 14 pages. doi:10.1155/2013/732178. https://projecteuclid.org/euclid.jam/1394807820


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