Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT
- Authors
- Nam, Haewon; Guo, Minghao; Yu, Hengyong; Lee, Keumsil; Li, Ruijiang; Han, Bin; Xing, Lei; Lee, Rena; Gao, Hao
- Issue Date
- 11-Jan-2019
- Publisher
- PUBLIC LIBRARY SCIENCE
- Citation
- PLOS ONE, v.14, no.1
- Journal Title
- PLOS ONE
- Volume
- 14
- Number
- 1
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/2045
- DOI
- 10.1371/journal.pone.0210410
- ISSN
- 1932-6203
- Abstract
- In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.
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Collections - College of Science and Technology > Department of Computer and Information Communications Engineering > 1. Journal Articles
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