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A generalized hybrid algorithm for bioluminescence tomography

Bioluminescence tomography (BLT) is a promising optical molecular imaging technique on the frontier of biomedical optics. In this paper, a generalized hybrid algorithm has been proposed based on the graph cuts algorithm and gradient-based algorithms. The graph cuts algorithm is adopted to estimate a...

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Detalles Bibliográficos
Autores principales: Shi, Shengkun, Mao, Heng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Optical Society of America 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3646598/
https://www.ncbi.nlm.nih.gov/pubmed/23667787
http://dx.doi.org/10.1364/BOE.4.000709
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author Shi, Shengkun
Mao, Heng
author_facet Shi, Shengkun
Mao, Heng
author_sort Shi, Shengkun
collection PubMed
description Bioluminescence tomography (BLT) is a promising optical molecular imaging technique on the frontier of biomedical optics. In this paper, a generalized hybrid algorithm has been proposed based on the graph cuts algorithm and gradient-based algorithms. The graph cuts algorithm is adopted to estimate a reliable source support without prior knowledge, and different gradient-based algorithms are sequentially used to acquire an accurate and fine source distribution according to the reconstruction status. Furthermore, multilevel meshes for the internal sources are used to speed up the computation and improve the accuracy of reconstruction. Numerical simulations have been performed to validate this proposed algorithm and demonstrate its high performance in the multi-source situation even if the detection noises, optical property errors and phantom structure errors are involved in the forward imaging.
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spelling pubmed-36465982013-05-10 A generalized hybrid algorithm for bioluminescence tomography Shi, Shengkun Mao, Heng Biomed Opt Express Image Reconstruction and Inverse Problems Bioluminescence tomography (BLT) is a promising optical molecular imaging technique on the frontier of biomedical optics. In this paper, a generalized hybrid algorithm has been proposed based on the graph cuts algorithm and gradient-based algorithms. The graph cuts algorithm is adopted to estimate a reliable source support without prior knowledge, and different gradient-based algorithms are sequentially used to acquire an accurate and fine source distribution according to the reconstruction status. Furthermore, multilevel meshes for the internal sources are used to speed up the computation and improve the accuracy of reconstruction. Numerical simulations have been performed to validate this proposed algorithm and demonstrate its high performance in the multi-source situation even if the detection noises, optical property errors and phantom structure errors are involved in the forward imaging. Optical Society of America 2013-04-10 /pmc/articles/PMC3646598/ /pubmed/23667787 http://dx.doi.org/10.1364/BOE.4.000709 Text en © 2013 Optical Society of America http://creativecommons.org/licenses/by-nc-nd/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License, which permits download and redistribution, provided that the original work is properly cited. This license restricts the article from being modified or used commercially.
spellingShingle Image Reconstruction and Inverse Problems
Shi, Shengkun
Mao, Heng
A generalized hybrid algorithm for bioluminescence tomography
title A generalized hybrid algorithm for bioluminescence tomography
title_full A generalized hybrid algorithm for bioluminescence tomography
title_fullStr A generalized hybrid algorithm for bioluminescence tomography
title_full_unstemmed A generalized hybrid algorithm for bioluminescence tomography
title_short A generalized hybrid algorithm for bioluminescence tomography
title_sort generalized hybrid algorithm for bioluminescence tomography
topic Image Reconstruction and Inverse Problems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3646598/
https://www.ncbi.nlm.nih.gov/pubmed/23667787
http://dx.doi.org/10.1364/BOE.4.000709
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