A fast reconstruction algorithm for fluorescence diffusion optical tomography

Xiaolei Song, Jing Bai, Xiaoyun Xiong, Zhaotian Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fluorescence Optical Diffusion Tomography (FODT) is considered as one of the most promising ways for non-invasive molecular-based Imaging. Many reconstructed approaches to FODT utilize iterative methods for data inversion. However, they are regarded as being time-consuming and far from meeting the real-time imaging requests. In this work, a fast pre-iteration algorithm based on the generalized inverse is established, which divides the image reconstruction into two steps that are off-line pre-iteration and on-line one-step reconstruction. In the pre-iteration step for obtaining the approximation of generalized inverse, a second order iterative format is employed to accelerate the convergence. Simulation based on the linear diffusion model shows that the distribution of fluorescent yield can be well estimated by this algorithm with second-order iteration. And the reconstructed speed is remarkably increased. Time-efficiency of this method will potentially promote the development of real-time imaging and the dynamic monitoring of molecular activity.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages1476-1479
Number of pages4
StatePublished - Dec 1 2005
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
CountryChina
CityShanghai
Period9/1/059/4/05

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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