Compressed sensing inspired image reconstruction from overlapped projections

Authors:
Lin Yang;Yang Lu;Ge Wang
Affiliations:
VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA and Department of Electrical and Computer Engineering, The Cooper Union for the Advancement of Science and Art, ...;VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA and Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA
Venue:
Journal of Biomedical Imaging
Year:
2010

Citing 3
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SART-type image reconstruction from overlapped projections

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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.