UPGRADE-Net: Unsupervised Sinogram-Domain Data-Consistent Network for Metal Artifact Reduction.

Abstract

Computed tomography (CT) scanners are widely used to obtain detailed internal images in clinical diagnosis. Highly attenuated metallic implants resulting from strong and energy-dependent attenuation cause metal artifacts in CT scanning. However, current supervised deep network-based metal artifact reduction (MAR) methods hardly generalize in clinical diagnosis and treatment because of difficult acquisition for the paired artifact-affected and artifact-free data. In addition, these deep model-based methods cannot ensure the sinogram-domain data consistency for the exact metal trace inpainting. To address the above problems, we propose an UnsuPervised sinoGRam-domAin Data-consistEnt network for MAR, i.e., UPGRADE-Net. First, UPGRADE-Net fully leverages the prior knowledge to guide the generative conditional diffusion model for fine-grained metal trace inpainting. Second, without the artifact-free ground truth, a deep unsupervised MAR framework in the reverse process is constructed to contextually learn the known background data distribution for the unknown metal trace restoration in sinogram-domain. Third, to further maintain the sinogram-domain data consistency, two physics-based consistency constraint loss functions, including conjugate-ray and accumulation-ray consistency loss, are designed for the conjugate point constraint and the accumulation constraint. The proposed UPGRADE-Net is trained and evaluated on a publicly available dataset and a clinical dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods for MAR.