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Bioluminescence tomography with Gaussian prior

Parameterizing the bioluminescent source globally in Gaussians provides several advantages over voxel representation in bioluminescence tomography. It is mathematically unique to recover Gaussians [Med. Phys. 31(8), 2289 (2004)] and practically sufficient to approximate various shapes by Gaussians i...

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Detalles Bibliográficos
Autores principales: Gao, Hao, Zhao, Hongkai, Cong, Wenxiang, Wang, Ge
Formato: Texto
Lenguaje:English
Publicado: Optical Society of America 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018113/
https://www.ncbi.nlm.nih.gov/pubmed/21258547
http://dx.doi.org/10.1364/BOE.1.001259
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author Gao, Hao
Zhao, Hongkai
Cong, Wenxiang
Wang, Ge
author_facet Gao, Hao
Zhao, Hongkai
Cong, Wenxiang
Wang, Ge
author_sort Gao, Hao
collection PubMed
description Parameterizing the bioluminescent source globally in Gaussians provides several advantages over voxel representation in bioluminescence tomography. It is mathematically unique to recover Gaussians [Med. Phys. 31(8), 2289 (2004)] and practically sufficient to approximate various shapes by Gaussians in diffusive medium. The computational burden is significantly reduced since much fewer unknowns are required. Besides, there are physiological evidences that the source can be modeled by Gaussians. The simulations show that the proposed model and algorithm significantly improves accuracy and stability in the presence of Gaussian or non- Gaussian sources, noisy data or the optical background mismatch. It is also validated through in vivo experimental data.
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spelling pubmed-30181132011-01-21 Bioluminescence tomography with Gaussian prior Gao, Hao Zhao, Hongkai Cong, Wenxiang Wang, Ge Biomed Opt Express Image Reconstruction and Inverse Problems Parameterizing the bioluminescent source globally in Gaussians provides several advantages over voxel representation in bioluminescence tomography. It is mathematically unique to recover Gaussians [Med. Phys. 31(8), 2289 (2004)] and practically sufficient to approximate various shapes by Gaussians in diffusive medium. The computational burden is significantly reduced since much fewer unknowns are required. Besides, there are physiological evidences that the source can be modeled by Gaussians. The simulations show that the proposed model and algorithm significantly improves accuracy and stability in the presence of Gaussian or non- Gaussian sources, noisy data or the optical background mismatch. It is also validated through in vivo experimental data. Optical Society of America 2010-10-29 /pmc/articles/PMC3018113/ /pubmed/21258547 http://dx.doi.org/10.1364/BOE.1.001259 Text en ©2010 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
Gao, Hao
Zhao, Hongkai
Cong, Wenxiang
Wang, Ge
Bioluminescence tomography with Gaussian prior
title Bioluminescence tomography with Gaussian prior
title_full Bioluminescence tomography with Gaussian prior
title_fullStr Bioluminescence tomography with Gaussian prior
title_full_unstemmed Bioluminescence tomography with Gaussian prior
title_short Bioluminescence tomography with Gaussian prior
title_sort bioluminescence tomography with gaussian prior
topic Image Reconstruction and Inverse Problems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018113/
https://www.ncbi.nlm.nih.gov/pubmed/21258547
http://dx.doi.org/10.1364/BOE.1.001259
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