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Proceedings Paper

Low-dose computed tomography image reconstruction via structure tensor total variation regularization
Author(s): Junfeng Wu; Xuanqin Mou; Yongyi Shi; Ti Bai; Yang Chen
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Paper Abstract

The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).

Paper Details

Date Published: 9 March 2018
PDF: 10 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733D (9 March 2018); doi: 10.1117/12.2293266
Show Author Affiliations
Junfeng Wu, Xi'an Univ. of Technology (China)
Southeast Univ. and Ministry of Education (China)
Xuanqin Mou, Xi'an Jiaotong Univ. (China)
Yongyi Shi, Xi'an Jiaotong Univ. (China)
Ti Bai, Xi'an Jiaotong Univ. (China)
Yang Chen, Southeast Univ. and Ministry of Education (China)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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