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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editor: Gregory W. Faris
  • Vol. 1, Iss. 7 — Jul. 17, 2006

Data subset algorithm for computationally efficient reconstruction of 3-D spectral imaging in diffuse optical tomography

Subhadra Srinivasan, Brian W. Pogue, Hamid Dehghani, Frederic Leblond, and Xavier Intes  »View Author Affiliations

Optics Express, Vol. 14, Issue 12, pp. 5394-5410 (2006)

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Three-dimensional (3-D) models of light propagation in diffuse optical tomography provide an accurate representation of scattering in tissue. Here the use of spectral priors, shown to improve quantification of functional parameters in 2-D, has been extended to 3-D. To make 3-D spectral imaging computationally tractable, a novel technique is presented to deal with the large data set. The basic principle consists of using a dynamic criterion to select optimal data subsets that capture the major changes in the imaging domain. Results from three test cases showed comparable image quality and accuracy with less than 4% difference between the uses of data subset approach versus the entire dataset. Tested on simulated data from two different models, the algorithm was able to discern multiple objects successfully with an average error of 30% in quantifying multiple regions and less than 1% in quantifying the background.

© 2006 Optical Society of America

OCIS Codes
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.6960) Medical optics and biotechnology : Tomography

ToC Category:
Medical Optics and Biotechnology

Original Manuscript: April 5, 2006
Revised Manuscript: May 19, 2006
Manuscript Accepted: May 25, 2006
Published: June 12, 2006

Virtual Issues
Vol. 1, Iss. 7 Virtual Journal for Biomedical Optics

Subhadra Srinivasan, Brian W. Pogue, Hamid Dehghani, Frederic Leblond, and Xavier Intes, "Data subset algorithm for computationally efficient reconstruction of 3-D spectral imaging in diffuse optical tomography," Opt. Express 14, 5394-5410 (2006)

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