LOR-based reconstruction for super-resolved 3D PET image

PET images usually suffer from low spatial resolution due to positron range, photon non-collinearity, scatters inside scintillating crystals, finite dimension of crystals, and so on. To improve the spatial resolution based on wobble scanning, we previously proposed a sinogram-based super-resolution (SR) algorithm based on a space-variant blur matrix. However, the algorithm may cause unwanted resolution loss due to an inevitable interpolation process for preparing even-spaced sinograms. In this paper, we propose a novel and efficient one-step line of response (LOR) based SR framework for 3D PET images. In the framework, we efficiently determine a large number of space-variant PSFs in an image space by using the scanner symmetries and the proposed PSF interpolation scheme based on non-rigid registration. We then obtain a HR image via one-step super-resolved 3D PET image reconstruction with the determined PSFs. Furthermore, we reduce the computational time of GPU-based reconstruction by introducing a parallel-friendly cone-beam based LOR system matrix. The proposed framework provides noticeable improvement on the spatial resolution of PET images with a considerable reduction of computational time.