A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration

A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration

Fluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are...

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Main Authors: Xiaolei Song, Xiaoyun Xiong, Jing Bai
Format: Article
Language:English
Published: Hindawi Limited 2007-01-01
Series:International Journal of Biomedical Imaging
Online Access:http://dx.doi.org/10.1155/2007/23219
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spelling doaj-b836198a272143998d3bf2634199c94f2020-11-24T22:57:44ZengHindawi LimitedInternational Journal of Biomedical Imaging1687-41881687-41962007-01-01200710.1155/2007/2321923219A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on PreiterationXiaolei Song0Xiaoyun Xiong1Jing Bai2Department of Biomedical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment 2, College of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, ChinaDepartment of Biomedical Engineering, Tsinghua University, Beijing 100084, ChinaFluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are far from meeting the real-time imaging demands. In this work, a fast preiteration algorithm based on the generalized inverse matrix is proposed. This method needs only one step of matrix-vector multiplication online, by pushing the iteration process to be executed offline. In the preiteration process, the second-order iterative format is employed to exponentially accelerate the convergence. Simulations based on an analytical diffusion model show that the distribution of fluorescent yield can be well estimated by this algorithm and the reconstructed speed is remarkably increased.http://dx.doi.org/10.1155/2007/23219
collection DOAJ
language English
format Article
sources DOAJ
author Xiaolei Song
Xiaoyun Xiong
Jing Bai
spellingShingle Xiaolei Song
Xiaoyun Xiong
Jing Bai
A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration
International Journal of Biomedical Imaging
author_facet Xiaolei Song
Xiaoyun Xiong
Jing Bai
author_sort Xiaolei Song
title A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration
title_short A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration
title_full A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration
title_fullStr A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration
title_full_unstemmed A Fast Reconstruction Algorithm for Fluorescence Optical Diffusion Tomography Based on Preiteration
title_sort fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration
publisher Hindawi Limited
series International Journal of Biomedical Imaging
issn 1687-4188
1687-4196
publishDate 2007-01-01
description Fluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are far from meeting the real-time imaging demands. In this work, a fast preiteration algorithm based on the generalized inverse matrix is proposed. This method needs only one step of matrix-vector multiplication online, by pushing the iteration process to be executed offline. In the preiteration process, the second-order iterative format is employed to exponentially accelerate the convergence. Simulations based on an analytical diffusion model show that the distribution of fluorescent yield can be well estimated by this algorithm and the reconstructed speed is remarkably increased.
url http://dx.doi.org/10.1155/2007/23219
work_keys_str_mv AT xiaoleisong afastreconstructionalgorithmforfluorescenceopticaldiffusiontomographybasedonpreiteration
AT xiaoyunxiong afastreconstructionalgorithmforfluorescenceopticaldiffusiontomographybasedonpreiteration
AT jingbai afastreconstructionalgorithmforfluorescenceopticaldiffusiontomographybasedonpreiteration
AT xiaoleisong fastreconstructionalgorithmforfluorescenceopticaldiffusiontomographybasedonpreiteration
AT xiaoyunxiong fastreconstructionalgorithmforfluorescenceopticaldiffusiontomographybasedonpreiteration
AT jingbai fastreconstructionalgorithmforfluorescenceopticaldiffusiontomographybasedonpreiteration
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