Compressed sensing metal artifact removal in dental CT

Abstract

Metal artifact removal (MAR) has been an important issue in dental X-ray CT due to the presence of metal implant and fillings. The practical use of most existing MAR methods have limitations due to their inherent drawbacks. In this research, we propose a novel MAR algorithm in dental CT. Based on the sparse volume occupation of the metallic inserts, we can formulate the MAR problem as a sparse recovery problem within the compressed sensing framework. One of the main advantages of employing compressed sensing theory in MAR problem is that the sparseness of the metallic objects allows us to reduce the view samples significantly without loss of image quality, accelerating the proposed MAR algorithm drastically. Experimental results using real dental CT scanner measurements show that our algorithm can perform accurate metallic artifact removal very quickly.