@inproceedings{c453869444ef404baeab9d0a99dcc867,
title = "Low-dose computed tomography image reconstruction via structure tensor total variation regularization",
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).",
keywords = "Low dose CT, proximal forward-backward splitting, structure tensor total variation",
author = "Junfeng Wu and Xuanqin Mou and Yongyi Shi and Ti Bai and Yang Chen",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Physics of Medical Imaging ; Conference date: 12-02-2018 Through 15-02-2018",
year = "2018",
doi = "10.1117/12.2293266",
language = "英语",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Schmidt, \{Taly Gilat\} and Guang-Hong Chen and Lo, \{Joseph Y.\}",
booktitle = "Medical Imaging 2018",
}