Rethinking PET Image Reconstruction: Ultra-Low-Dose, Sinogram and Deep Learning

Abstract

Although Positron emission tomography (PET) has a wide range of clinical applications, radiation exposure to patients in PET continues to draw concerns. To reduce the radiation risk, efforts have been made to obtain high resolution images from low-resolution images. However, previous studies mainly focused on denoising PET images in image space, which ignored the influence of sinogram quality and constraints in reconstruction process. This paper proposed a directly reconstruction framework from ultra-low-dose sinogram based on deep learning. Two coupled networks are introduced to sequentially denoise low-dose sinogram and reconstruct the activity map. Evaluation on in vivo PET dataset indicates that the proposed method can achieve better performance than other state-of-the-art methods and reconstruct satisfactory PET images with only 0.2% dose of standard one.