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BioMed Research International
Volume 2016 (2016), Article ID 3094698, 9 pages
Research Article

An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China

Received 20 April 2016; Accepted 14 July 2016

Academic Editor: Shouping Zhu

Copyright © 2016 Luzhen Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Compressive Sensing (CS) theory has great potential for reconstructing Computed Tomography (CT) images from sparse-views projection data and Total Variation- (TV-) based CT reconstruction method is very popular. However, it does not directly incorporate prior images into the reconstruction. To improve the quality of reconstructed images, this paper proposed an improved TV minimization method using prior images and Split-Bregman method in CT reconstruction, which uses prior images to obtain valuable previous information and promote the subsequent imaging process. The images obtained asynchronously were registered via Locally Linear Embedding (LLE). To validate the method, two studies were performed. Numerical simulation using an abdomen phantom has been used to demonstrate that the proposed method enables accurate reconstruction of image objects under sparse projection data. A real dataset was used to further validate the method.