SPARSE REPRESENTATION IN THE RECONSTRUCTION OF ULTRA-HIGH-RESOLUTION PET IMAGES

Abstract

Reconstructed pictures in positron emission tomography (PET) research are frequently noisy and low quality. The low-count issue is the main source of these issues. Sparse prediction is more likely to be chosen as the solution as sparse technology is employed more frequently. I suggest a brand-new sparse prior technique in this research to process low quality PET reconstructed pictures. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are trained from plenty of actual PET image patches in the proposed approach. The sparse representation for each patch of the input PET picture is then obtained using D1. Finally, D2 is used to create a high-resolution PET image from this sparse representation. The results of the studies show that the suggested strategy has superior effects that improve image resolution and detail recovery that are stable. In terms of root mean square error, this technique performs better quantitatively than older methods (RMSE). The suggested method offers a fresh and effective way to enhance the image quality of PET reconstructions. The back projection technique was created for magnetic resonance imaging (MRI) picture reconstruction of under sampled data and has been used to denoise dynamic PET images. Keywords: PET imaging, data acquisition, artifacts, resolution, information technologies.