Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 185750, 15 pages
Research Article

Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction

1Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada S7N 5A9
2Department of Medical Imaging, Saskatoon Health Region, Saskatoon, Canada S7N 0W8
3College of Medicine, University of Saskatchewan, Saskatoon, Canada S7N 5E5

Received 10 January 2013; Revised 4 March 2013; Accepted 5 March 2013

Academic Editor: Wenxiang Cong

Copyright © 2013 Zangen Zhu 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.


In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: the norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.