Abstract
A new deep-learning approach, dual-domain diffusion model (DDDM), is proposed for sparse-view CT reconstruction, which is composed of a sinogram upgrading module (SUM) and an image refining module (IRM) connected in series. In the sinogram domain, a novel degrading and upgrading framework is defined, in which SUM is trained to upgrade sparse-view sinograms step by step to reverse the degradation process of CT images caused by successive down-sampling of scanning views. In the image domain, IRM adopts an improved denoising diffusion framework to further reduce remaining artifacts and restore image details, where a skip connection from the original sparse-view sinogram is introduced to constrain the generation of details. Our DDDM shows significant improvement over deep-learning baseline models in both classical similarity metrics and perceptual loss, and has good generalization to untrained organs. We release our code at: https://github.com/YC-Markus/code-for-DDDM.