Super-resolution in PET images using space variant kernels

TL;DRAbstract

268 Objectives Positron emission tomography (PET) images suffer from limited spatial resolution. In order to overcome this problem, several super-resolution (SR) algorithms were suggested. However, the existing PET image SR algorithms do not consider the problem of space variant resolution. In this paper, we present an image-domain SR scheme by applying space variant blur kernels. Methods SR approach synthesizes a high-resolution (HR) image by using multiple low-resolution (LR) images. To obtain the relationship between HR image and LR images for SR, we need a point spread function (PSF) at each HR image pixel position. To obtain PSFs, we first acquire a sinogram of a point source at every pixel position. Using a sinogram, we then reconstruct an image to obtain a PSF or a blur kernel at the corresponding position. We use the ordered subsets expectation maximization (OSEM) algorithm for image reconstruction. A HR image is synthesized utilizing those space variant blur kernels in the SR

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