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Proceedings Paper

Comparing two regularization techniques for diffuse optical tomography
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Paper Abstract

Two techniques to regularize the diffuse optical tomography inverse problem were compared for a variety of simulated test domains. One method repeats the single-step Tikhonov approach until a stopping criteria is reached, regularizing the inverse problem by scaling the maximum of the diagonal of the inversion matrix with a factor held constant throughout the iterative reconstruction. The second method, a modified Levenberg-Marquardt formulation, uses an identical implementation but reduces the factor at each iteration. Four test geometries of increasing complexity were used to test the performance of the two techniques under a variety of conditions including varying amounts of data noise, different initial parameter estimates, and different initial values of the regularization factor. It was found that for most cases tested, holding the scaling factor constant provided images that were more robust to both data noise and initial homogeneous parameter estimates. However, the results for a complex test domain that most resembled realistic tissue geometries were less conclusive.

Paper Details

Date Published: 13 February 2007
PDF: 12 pages
Proc. SPIE 6434, Optical Tomography and Spectroscopy of Tissue VII, 64340X (13 February 2007); doi: 10.1117/12.700364
Show Author Affiliations
Scott C. Davis, Dartmouth College (United States)
Hamid Dehghani, Dartmouth College (United States)
Univ. of Exeter (United Kingdom)
Phaneendra K. Yalavarthy, Dartmouth College (United States)
Brian W. Pogue, Dartmouth College (United States)
Keith D. Paulsen, Dartmouth College (United States)


Published in SPIE Proceedings Vol. 6434:
Optical Tomography and Spectroscopy of Tissue VII
Britton Chance; Robert R. Alfano; Bruce J. Tromberg; Mamoru Tamura; Eva Marie Sevick-Muraca, Editor(s)

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