Compressed Sensing Inspired Image Reconstruction from Overlapped Projections
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 ap...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2010/284073 |
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| _version_ | 1849304572557787136 |
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| author | Lin Yang Yang Lu Ge Wang |
| author_facet | Lin Yang Yang Lu Ge Wang |
| author_sort | Lin Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e47c3e262a54860a15932da19073d6a |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-3e47c3e262a54860a15932da19073d6a2025-08-20T03:55:41ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962010-01-01201010.1155/2010/284073284073Compressed Sensing Inspired Image Reconstruction from Overlapped ProjectionsLin Yang0Yang Lu1Ge Wang2Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA 24061, USABiomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA 24061, USABiomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA 24061, USAThe 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.http://dx.doi.org/10.1155/2010/284073 |
| spellingShingle | Lin Yang Yang Lu Ge Wang Compressed Sensing Inspired Image Reconstruction from Overlapped Projections International Journal of Biomedical Imaging |
| title | Compressed Sensing Inspired Image Reconstruction from Overlapped Projections |
| title_full | Compressed Sensing Inspired Image Reconstruction from Overlapped Projections |
| title_fullStr | Compressed Sensing Inspired Image Reconstruction from Overlapped Projections |
| title_full_unstemmed | Compressed Sensing Inspired Image Reconstruction from Overlapped Projections |
| title_short | Compressed Sensing Inspired Image Reconstruction from Overlapped Projections |
| title_sort | compressed sensing inspired image reconstruction from overlapped projections |
| url | http://dx.doi.org/10.1155/2010/284073 |
| work_keys_str_mv | AT linyang compressedsensinginspiredimagereconstructionfromoverlappedprojections AT yanglu compressedsensinginspiredimagereconstructionfromoverlappedprojections AT gewang compressedsensinginspiredimagereconstructionfromoverlappedprojections |