Sinogram-based super-resolution in PET

Abstract

Spatial resolution is intrinsically limited in positron emission tomography (PET) systems, mainly due to the crystal width. To increase the spatial resolution for a given crystal width, mechanical movements such as wobble and dichotomic motions are introduced to the PET systems. However, multiple sinograms obtained through such movements provide oversampled data. In this paper, to increase the spatial resolution, we present a novel super-resolution (SR) scheme that employs multiple sinograms. For SR, we first propose a blur kernel estimation scheme through a Monte Carlo simulation. Based on the estimated blur kernel, we adopt a maximum a posteriori expectation maximization method in estimating a high-resolution sinogram from multiple low-resolution sinograms. The proposed algorithm provides noticeable improvement of the spatial resolution in real PET images.