Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT

Haewon Nam, Minghao Guo, Hengyong Yu, Keumsil Lee, Ruijiang Li, Bin Han, Lei Xing, Rena Lee, Hao Gao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article numbere0210410
JournalPLoS ONE
Issue number1
StatePublished - Jan 2019

Bibliographical note

Publisher Copyright:
© 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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