Unsupervised clustering of dynamic PET images on the projection domain

Abstract

Segmentation of dynamic PET images is an important preprocessing step for kinetic parameter estimation. A single time activity curve (TAC) is extracted for each segmented region. This TAC is then used to estimate the kinetic parameters of the segmented region. Current methods perform this task in two independent steps; first dynamic positron emission tomography (PET) images are reconstructed from the projection data using conventional tomographic reconstruction methods, then the time activity curves (TAC) of the pixels are clustered into a predetermined number of clusters. In this paper, we propose to cluster the regions of dynamic PET images directly on the projection data and simultaneously estimate the TAC of each cluster. This method does not require an intermediate step of tomographic reconstruction for each time frame. Therefore the dimensionality of the estimation problem is reduced. We compare the proposed method with weighted least squares (WLS) and expectation maximization with Gaussian mixtures methods (GMM-EM). Filtered backprojection is used to reconstruct the emission images required by these methods. Our simulation results show that the proposed method can substantially decrease the number of mislabeled pixels and reduce the root mean squared error (RMSE) of the cluster TACs.