Affiliations: Changchun Institute of Optics, Fine Mechanics and
Physics, Chinese Academy of Sciences, Changchun, Jilin, China | University of Chinese Academy of Sciences, Beijing,
China | Medical Imaging Department, Suzhou Institute of
Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou,
Jiangsu, China | PET Center, Huashan Hospital, Fudan University,
Shanghai, China
Note: [] Corresponding author: Jian Zheng, Medical Imaging Department,
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of
Sciences, Suzhou 215163, Jiangsu, China. E-mail: zhengj@sibet.ac.cn
Abstract: In computed tomography (CT), metal objects in the scanning filed are
accompanied by physical phenomenon that causes projections to be inconsistent.
These inconsistencies produce bright and dark shadows or streaks in
analytically reconstructed images. Interpolation-based metal artifact reduction
(MAR) algorithms usually replace the inconsistent projection data by estimating
surrogate data based on the surrounding uncorrupted projections. In such cases,
secondary artifacts will be generated when the data estimates are inaccurate.
Therefore, better projection estimation is critical. This paper proposes an
image post-processing strategy to create an intermediate image, named the prior
image and better estimates of the surrogate data by forward projecting this
prior image. The proposed method consists of three steps based on the forward
projection MAR framework. First, metallic implants in the uncorrected images
are segmented using a Markov random field model (MRF). Then a prior image is
generated via an edge-preserving filter and a recovery procedure of the
adjacent anatomical structures. Finally, the projection is completed via
forward projecting this prior image and the corrected image is reconstructed by
the filtered backprojection (FBP) method. Studies on both phantom and clinical
data are carried out to verify the performance of the proposed method. The
comparisons with other previous MAR algorithms demonstrate that the proposed
MAR method performs better in metal artifact suppression and anatomical
structure preservation.
Keywords: Computed tomography, metal artifact reduction, secondary artifacts, prior image
