DPD-Net: a dual-domain progressive diffusion network for joint limited-angle and metal artifact removal

Computed Tomography (CT) is a high-precision medical imaging technique that utilizes X-rays and computer reconstruction to provide detailed three-dimensional images of human anatomy. It is used for clinical diagnosis and treatment. Non-ideal scanning conditions often occur, including the presence of metal implants in the human body and limited-angle scanning. These non-ideal conditions result in serious metal artifacts and limited-angle artifacts. To address the above challenge, in this paper, we propose a novel deep dual-domain progressive diffusion network, namely DPD-Net, to jointly suppress metal artifact and limited-angle artifact for the first time. DPD-Net leverages the advantage of dual-domain strategy for limited-angle artifact suppression in image-domain and metal trace inpainting in sinogram-domain simultaneously. To sufficiently solve dual-artifact problem, the dual-domain generative diffusion models are designed for data distribution learning. The proposed DPD-Net is trained and evaluated on a publicly available dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods.