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Journal of Applied Mathematics
Volume 2013 (2013), Article ID 732178, 14 pages
http://dx.doi.org/10.1155/2013/732178
Research Article

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

1Department of Electronics and Informatics (ETRO-IRIS), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
2Faculty of Information Technology and Computer Engineering, Palestine Polytechnic University, Hebron, Palestine
3Interuniversity Microelectronics Centre (IMEC), Leuven, Belgium

Received 23 March 2013; Revised 4 May 2013; Accepted 4 May 2013

Academic Editor: Hang Joon Jo

Copyright © 2013 Musa Alrefaya and Hichem Sahli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.