Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging
Image reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, st...
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-03-01
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| Series: | Algorithms |
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| Online Access: | http://www.mdpi.com/1999-4893/6/1/136 |
| _version_ | 1819104682511958016 |
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| author | Jun Ma |
| author_facet | Jun Ma |
| author_sort | Jun Ma |
| collection | DOAJ |
| description | Image reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, statistical reconstruction methods are preferred. In this paper we review some non-negatively constrained simultaneous iterative algorithms for maximum penalized likelihood reconstructions, where all measurements are used to estimate all pixel intensities in each iteration. |
| first_indexed | 2024-12-22T02:10:14Z |
| format | Article |
| id | doaj.art-8168751e751948bb8dead4b59191f39e |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-22T02:10:14Z |
| publishDate | 2013-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-8168751e751948bb8dead4b59191f39e2022-12-21T18:42:24ZengMDPI AGAlgorithms1999-48932013-03-016113616010.3390/a6010136Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic ImagingJun MaImage reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, statistical reconstruction methods are preferred. In this paper we review some non-negatively constrained simultaneous iterative algorithms for maximum penalized likelihood reconstructions, where all measurements are used to estimate all pixel intensities in each iteration.http://www.mdpi.com/1999-4893/6/1/136tomographic imagingpenalized likelihoodalgorithmsconstrained optimization |
| spellingShingle | Jun Ma Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging Algorithms tomographic imaging penalized likelihood algorithms constrained optimization |
| title | Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging |
| title_full | Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging |
| title_fullStr | Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging |
| title_full_unstemmed | Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging |
| title_short | Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging |
| title_sort | algorithms for non negatively constrained maximum penalized likelihood reconstruction in tomographic imaging |
| topic | tomographic imaging penalized likelihood algorithms constrained optimization |
| url | http://www.mdpi.com/1999-4893/6/1/136 |
| work_keys_str_mv | AT junma algorithmsfornonnegativelyconstrainedmaximumpenalizedlikelihoodreconstructionintomographicimaging |