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International Journal of Biomedical Imaging
Volume 2010, Article ID 284073, 8 pages
http://dx.doi.org/10.1155/2010/284073
Research Article

Compressed Sensing Inspired Image Reconstruction from Overlapped Projections

Lin Yang,1,2 Yang Lu,1,3 and Ge Wang1

1Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA 24061, USA
2Department of Electrical and Computer Engineering, The Cooper Union for the Advancement of Science and Art, New York, NY 10003, USA
3Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received 14 December 2009; Accepted 12 April 2010

Academic Editor: Lizhi Sun

Copyright © 2010 Lin Yang 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.

Abstract

The key idea discussed in this paper is to reconstruct an image from overlapped projections so that the data acquisition process can be shortened while the image quality remains essentially uncompromised. To perform image reconstruction from overlapped projections, the conventional reconstruction approach (e.g., filtered backprojection (FBP) algorithms) cannot be directly used because of two problems. First, overlapped projections represent an imaging system in terms of summed exponentials, which cannot be transformed into a linear form. Second, the overlapped measurement carries less information than the traditional line integrals. To meet these challenges, we propose a compressive sensing-(CS-) based iterative algorithm for reconstruction from overlapped data. This algorithm starts with a good initial guess, relies on adaptive linearization, and minimizes the total variation (TV). Then, we demonstrated the feasibility of this algorithm in numerical tests.