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

• Type of Material: Article
• Authors: Haewon Nam, Hongik University; Minghao Guo, Shanghai Jiao Tong University; Hengyong Yu, University of Massachusetts; Keumsil Lee, Stanford University; Ruijiang Li, Stanford University; Bin Han, Stanford University; Lei Xing, Stanford University; Rena Lee, Ewha Womans University; Hao Gao, Emory University
• Language: English
• Date: 2019-01-11
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2019 Nam et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0210410
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 14
• Issue: 1
• Start Page: e0210410
• End Page: e0210410
• Grant/Funding Information: H. Nam was supported by the Basic Science Research program through NRF (#2015R1C1A2A01054731) of Korea funded by the ministry of Education Science and Technology.; L. Xing is supported partially by NIH/NIBIB 1R01 EB-016777.; R. Lee was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea government (Ministry of Trade, Industry and Energy, No.0001723).
• Keywords: PI-LINES; CONE-BEAM CT; FILTERED BACKPROJECTION; IMPLEMENTATION; INVERSION ALGORITHM; QUALITY; SCANNER; ART; Multidisciplinary Sciences; FORMULA; Science & Technology; Science & Technology - Other Topics
• Research Categories: Engineering, Biomedical; Health Sciences, Oncology; Health Sciences, Radiology

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.