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 have very low signal-to-noise ratio (SNR). Therefore, the kinetic parameters estimated from these individual pixel TACs are not accurate, and these estimations may have very high spatial variance. To alleviate this problem, the pixels with similar kinetic characteristics are clustered into regions, and TACs of pixels within each region are averaged to increase the SNR. It is recently shown that it is better to cluster dynamic PET images in the sinogram domain than to cluster them in the reconstructed image domain [1]. In that study, the sinograms are assumed to have Poisson distribution. The clusters and TACs of the clusters are then chosen to maximize posterior probability of the measured sinograms. Although the raw sinogram data is Poisson distributed, the sino-gram data that is corrected for scatter, randoms, attenuation etc. is not Poisson distributed anymore. The corrected sinogram data can be better described using Gaussian distribution. In this paper, we describe how to cluster dynamic PET images on the sinogram domain when the sinograms are Gaussian distributed.