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 and Hao Gao

PLOS ONE, 2019, vol. 14, issue 1, 1-17

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0210410

DOI: 10.1371/journal.pone.0210410

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