Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2013, Article ID 185750, 15 pages http://dx.doi.org/10.1155/2013/185750 Research Article Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction Zangen Zhu, 1 Khan Wahid, 1 Paul Babyn, 2 David Cooper, 3 Isaac Pratt, 3 and Yasmin Carter 3 1 Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada S7N 5A9 2 Department of Medical Imaging, Saskatoon Health Region, Saskatoon, Canada S7N 0W8 3 College of Medicine, University of Saskatchewan, Saskatoon, Canada S7N 5E5 Correspondence should be addressed to Khan Wahid; khan.wahid@usask.ca Received 10 January 2013; Revised 4 March 2013; Accepted 5 March 2013 Academic Editor: Wenxiang Cong Copyright © 2013 Zangen Zhu et al. his 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 eicient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: the ℓ 1 norm, total variation, and a least squares measure. he 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. he results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods. 1. Introduction X-ray computed tomography (CT) is extensively used clin- ically to evaluate patients with a variety of conditions. However, by its nature, CT scans expose the patients to high X-ray radiation doses which can result in an increased lifetime risk of cancer [1, 2]. he radiation dose to the patients is proportional to the number of X-ray projections. Additionally, medical research makes extensive use of CT on the microscopic scale, known as micro-CT. Longitudinal studies on experimental animals such as rats, mice, and rabbits are also restricted in resolution and image quality by radiation dose. Currently, the defacto standard for recon- struction on the commercial CT scanners is the iltered backprojection (FBP) algorithm, which typically requires a large number (300–1000) of angular views for yielding accurate reconstruction of the image object. Recently a number of strategies have been proposed to decrease radiation dose in CT scans. One approach to lower the total X-ray radiation dose is to simply reduce the dose level mAs/view in data acquisition protocols. his approach typically results in an insuicient number of X-ray photons received by the detectors, increasing the noise level on the sinograms produced. he noise-contaminated sinogram data will degrade the quality of reconstructed CT images when a conventional FBP algorithm is used [3]. Another way to reduce the total radiation dose is to reduce the number of pro- jections needed. According to the standard image reconstruc- tion theory in image processing, when the number of the view angles does not satisfy the Shannon/Nyquist sampling the- orem, aliasing artifacts will spread out in the reconstructed images. As a consequence, FBP algorithms do not produce diagnostically satisfactory images in sparse-view data collec- tion schemes, because they are derived by assuming densely sampled projections over the scanning angular range. Since analytical reconstruction methods, such as FBP, cause such serious streaking artifacts in the resulting recon- structed CT images, iterative algorithms have been proposed