Low count PET sinogram denoising

Poisson noise is characteristic of count accumulation in positron emission tomography (PET) sinograms. We consider the denoising of low count PET sinograms by the following filters: block-matching and 3D (BM3D) filter, radial filter and stackgram filter. We also study the effect of first stabilizing the sinogram noise variance with Anscombe transformation, denoising the sinogram by the three methods and finally using exact unbiased inverse transformation to get back to the original sinogram domain. Quantitative results show that for the entire image the BM3D filter outperforms the other methods, but more careful region of interest (ROI) based analysis reveals that for the ROls in the central area of the image radial filtering with arithmetic mean filter gives the best quantitative results. Anscombe transformation has beneficial effect on BM3D filtering results. For the other filters its effect is negligible.