Affiliations: Department of Computational Science and Engineering,
Yonsei University, Seoul, Korea | Department of Dentistry, Yonsei University, Seoul,
Korea | Bio-Imaging and Signal Processing Laboratory,
Department of Bio and Brain Engineering, Korea Advanced Institute of Science
and Technology, Daejon, Korea
Note: [] Corresponding author: Jin Keun Seo, Department of Computational
Science and Engineering, Yonsei University, 134 Sinchon-dong Seodaemun-gu,
Seoul 120-749, Korea. E-mail: seoj@yonsei.ac.kr
Abstract: There is increasing demand in the field of dental and medical
radiography for effective metal artifact reduction (MAR) in computed tomography
(CT) because artifact caused by metallic objects causes serious image
degradation that obscures information regarding the teeth and/or other
biological structures. This paper presents a new MAR method that uses the
Laplacian operator to reveal background projection data hidden in regions
containing data from metal. In the proposed method, we attempted to decompose
the projection data into two parts: data from metal only (metal data), and
background data in the absence of metal. Removing metal data from the
projections enables us to perform sparsity-driven reconstruction of the metal
component and subsequent removal of the metal artifact. The results of clinical
experiments demonstrated that the proposed MAR algorithm improves image quality
and increases the standard of 3D reconstruction images of the teeth and mandible.
