ICA-Based noise reduction for PET Sinogram-Domain Images

Projection data in positron emission tomography (PET) are acquired as a number of photon counts from different observation angles. Positron decay is a random phenomenon that causes undesirably high variations in measured sinogram appearing as quantum noise. The ruduction of quantum noise or Poisson noise in medical images is an important issue. In this paper, we propose a new ICA-based filter for reduction of noise in sinogram domain. In the proposed filter, the sinogram (projection data) is firstly transformed to ICA domain, and then, the components of scattered projection are removed by a soft thresholding (Shrinkage). In this study, the choice of ICA basis function trained from different database is considered. The denoised results with different ICA basis function and conventional denoising method (wavelet shrinkage and Gaussian filter) are given for comparison, and then, we also show the reconstructed images of ICA-based denoised sinogram images using filtered-back-projection(FBP) algorithm. Experimental results show that the reconstructed images of ICA-based denoised images are much clearer and have much better contrast than those without pre-processing filters.