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 sinogramprereconstruction.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.
