Metal artifact reduction in CT using unsupervised sinogram manifold learning

Computed tomography (CT) imaging is widely used for medical diagnosis and image guidance for treatment. Metal artifacts are observed on the reconstructed CT images if metal implants are carried by patients due to the beam hardening effects. In this condition, the acquired projection data cannot be used for analytical reconstruction as they do not meet Tuy's data sufficiency condition. Numerous deep learning-based methods have been developed for metal artifact reduction (MAR), providing superior performance. Nevertheless, all the reported models are data-driven and require large-size referenced images for the manifold approximation. In this work, we propose a physics-driven sinogram manifold learning method, which fully exploits the projection data correlation in CT scanning for MAR, and the proposed method is ready to be extended to other data-incomplete CT reconstruction problems.