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Linear interpolation based structure preserved metal artifact reduction in x-ray computed tomography
Author(s): Huisu Yoon; Kyoung-Yong Lee
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Paper Abstract

In X-ray CT imaging, metal in imaging FOV deteriorates diagnostic quality of the reconstructed image. This is because rays penetrating dense metal implants are highly corrupted, resulting in huge inconsistency between projection data and the basic assumption of the image reconstruction principle is broken. For several decades, there have been various trials to address this problem. As computing power of computer processors increased, more complex algorithms with improved performance such as iterative reconstruction have been introduced. Recently, machine learning based techniques were introduced to the community. The purpose of this paper is to introduce a computationally effective MAR reducing severe metal artifacts while preserving fine internal structures. Thanks to its low cost, the algorithm can be carried out as a partial module of other MARs, or can be integrated into mobile CT scanners having a low computing power. The proposed algorithm adopts an idea based on a linear interpolation and it reduces severe artifacts such as black shadings well while not distorting neighboring structures. The proposed algorithm was integrated with a sinogram correction type MAR for better image quality. Results of our physical phantom experiment show that the proposed algorithm reduces metal artifacts effectively under low computational cost.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110722K (28 May 2019); doi: 10.1117/12.2534886
Show Author Affiliations
Huisu Yoon, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Kyoung-Yong Lee, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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