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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Bibliographic Details
Main Author:	Jun Ma
Format:	Article
Language:	English
Published:	MDPI AG 2013-03-01
Series:	Algorithms
Subjects:	tomographic imaging penalized likelihood algorithms constrained optimization
Online Access:	http://www.mdpi.com/1999-4893/6/1/136

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spelling	doaj-8168751e751948bb8dead4b59191f39e2020-11-25T00:57:19ZengMDPI 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
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jun Ma
spellingShingle	Jun Ma Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging Algorithms tomographic imaging penalized likelihood algorithms constrained optimization
author_facet	Jun Ma
author_sort	Jun Ma
title	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_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_sort	algorithms for non-negatively constrained maximum penalized likelihood reconstruction in tomographic imaging
publisher	MDPI AG
series	Algorithms
issn	1999-4893
publishDate	2013-03-01
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.
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
_version_	1725224681905061888

Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging

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