PURPOSE The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is present...

Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task. Supervised such as Dual Domain Network (Du-DoNet) work well simulation data; however, their performance clinical data is limited due to domain gap. Unsupervised are more generalized, but do not eliminate artifacts completely through sole processing image domai...

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most them suffer from two problems: 1) CT imaging geometry constraint is not fully embedded into network during training, leaving room for further performance improvement; 2) model interpretability lack sufficient consideration. Against these issues, we propose a novel...

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images. The method exploits the possibilitie...