A metal artifact reduction algorithm in CT using multiple prior imagesby recursive active contour segmentation

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473718.do
DOI
10.1371/journal.pone.0179022
Title
A metal artifact reduction algorithm in CT using multiple prior imagesby recursive active contour segmentation
Description
This research was supported by the MSIP (Ministry of Science, ICT andFuture Planning), Korea, under the "IT Consilience Creative Program"(IITP-2015-R0346-15-1008) supervised by the IITP (Institute forInformation & Communications Technology Promotion) and Basic ScienceResearch Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Science, ICT & Future Planning(2015R1C1A2A01054731, 2015R1C1A101052268, 2017M2A2A6A01019663).
abstract
We propose a novel metal artifact reduction (MAR) algorithm for CT images that completes a corrupted sinogram along the metal trace region. When metal implants are located inside a field of view, they create a barrier to the transmitted X-ray beam due to the high attenuation of metals, which significantly degrades the image quality. To fill in the metal trace region efficiently, the proposed algorithm uses multiple prior images with residual error compensation in sinogram space. Multiple prior images are generated by applying a recursive active contour (RAC) segmentation algorithm to the pre-corrected image acquired by MAR with linear interpolation, where the number of prior image is controlled by RAC depending on the object complexity. A sinogram basis is then acquired by forward projection of the prior images. The metal trace region of the original sinogram is replaced by the linearly combined sinogram of the prior images. Then, the additional correction in the metal trace region is performed to compensate the residual errors occurred by non-ideal data acquisition condition. The performance of the proposed MAR algorithm is compared with MAR with linear interpolation and the normalized MAR algorithm using simulated and experimental data. The results show that the proposed algorithm outperforms other MAR algorithms, especially when the object is complex with multiple bone objects.
provenance
Made available in Cube on 2018-09-28T10:27:02Z (GMT). No. of bitstreams: 0
language
English
author
Nam, Haewon
Baek, Jongduk
accessioned
2018-09-28T10:27:02Z
available
2018-09-28T10:27:02Z
issued
2017
citation
PLOS ONE(12): 6
issn
1932-6203
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473718.do
Funder
과학기술정보통신부
Funding Program
정보통신기술인력양성
Project ID
1711055182
Jurisdiction
Rep.of Korea
Project Name
Institute of Future Convergence Technology Initiative
rights
openAccess
type
article


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