Clustering dynamic PET images on the Gaussian distributed sinogram domain

Segmentation of dynamic PET images is an important preprocessing step for kinetic parameter estimation. The time activity curve (TAC) of individual pixels has very low signal-to-noise ratio (SNR). Therefore, the kinetic parameters estimated from the TAC of an individual pixel may not be accurate, and these estimations may have very high spatial variance. To alleviate this problem, pixels with similar kinetic characteristics are clustered into regions, and TACs of pixels within each region are averaged to increase SNR. It has recently been shown that clustering dynamic PET images in the sinogram domain is better than clustering them in the reconstructed image domain [M.E. Kamasak, B. Bayraktar, Clustering dynamic PET images on the projection domain, IEEE Trans. Nucl. Sci. 54 (3) (June 2007) 496-503.]. In that study, the sinograms are assumed to have Poisson distribution. The clusters and TACs of the clusters are then chosen to maximize the posterior probability of the measured sinograms. Although the raw sinogram data are Poisson distributed, the sinogram data corrected for scatter, randoms, attenuation etc. are not Poisson distributed anymore. In this paper, we describe how to cluster dynamic PET images on the sinogram domain when the sinograms are Gaussian distributed.