Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT
- 등재 SCIE, SCOPUS
- OA유형 Green Published, gold, Green Submitted
- 발행기관 Public Library of Science
- 발행년도 2019
- URI http://www.dcollection.net/handler/ewha/000000155942
- 본문언어 영어
- Published As http://dx.doi.org/10.1371/journal.pone.0210410
- PubMed https://pubmed.ncbi.nlm.nih.gov/30633760
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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. © 2019 Nam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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