Approximation errors and model reduction in three-dimensional diffuse optical tomography
JOSA A, Vol. 26, Issue 10, pp. 2257-2268 (2009)
http://dx.doi.org/10.1364/JOSAA.26.002257
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Abstract
Model reduction is often required in diffuse optical tomography (DOT), typically because of limited available computation time or computer memory. In practice, this means that one is bound to use coarse mesh and truncated computation domain in the model for the forward problem. We apply the (Bayesian) approximation error model for the compensation of modeling errors caused by domain truncation and a coarse computation mesh in DOT. The approach is tested with a three-dimensional example using experimental data. The results show that when the approximation error model is employed, it is possible to use mesh densities and computation domains that would be unacceptable with a conventional measurement error model.
© 2009 Optical Society of America
OCIS Codes
(100.3190) Image processing : Inverse problems
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.6960) Medical optics and biotechnology : Tomography
ToC Category:
Image Processing
History
Original Manuscript: June 5, 2009
Revised Manuscript: August 20, 2009
Manuscript Accepted: August 27, 2009
Published: September 25, 2009
Citation
Ville Kolehmainen, Martin Schweiger, Ilkka Nissilä, Tanja Tarvainen, Simon R. Arridge, and Jari P. Kaipio, "Approximation errors and model reduction in three-dimensional diffuse optical tomography," J. Opt. Soc. Am. A 26, 2257-2268 (2009)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-26-10-2257
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