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Biomed Opt Express. 2011 Mar 25;2(4):946-65. doi: 10.1364/BOE.2.000946.

Hyperspectral image reconstruction for diffuse optical tomography.

Abstract

We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths.

KEYWORDS:

(100.3190) Inverse problems; (170.3010) Image reconstruction techniques; (170.3660) Light propagation in tissues; (170.3830) Mammography; (170.3880) Medical and biological imaging; (170.5280) Photon migration; (170.6960) Tomography; (290.1990) Diffusion; (290.7050) Turbid media

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