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Improvement of image quality of time-domain diffuse optical tomography with lp sparsity regularization
Author(s) -
Shinpei Okawa,
Yoko Hoshi,
Yukio Yamada
Publication year - 2011
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.2.003334
Subject(s) - regularization (linguistics) , diffuse optical imaging , imaging phantom , algorithm , attenuation coefficient , image resolution , tomography , physics , image quality , mathematics , mathematical analysis , optics , computer science , artificial intelligence , image (mathematics)
An l(p) (0 < p ≤ 1) sparsity regularization is applied to time-domain diffuse optical tomography with a gradient-based nonlinear optimization scheme to improve the spatial resolution and the robustness to noise. The expression of the l(p) sparsity regularization is reformulated as a differentiable function of a parameter to avoid the difficulty in calculating its gradient in the optimization process. The regularization parameter is selected by the L-curve method. Numerical experiments show that the l(p) sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets, although a target with a small absorption coefficient may disappear due to the strong effect of the l(p) sparsity regularization when the value of p is too small. The l(p) sparsity regularization with small p values strongly localizes the target, and the reconstructed region of the target becomes smaller as the value of p decreases. A phantom experiment validates the numerical simulations.