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An Efficient Method for Model Reduction in Diffuse Optical Tomography
A.-R. Zirak, M. Khademi, M.-S. Mahloji,
Volume 4, Issue 1 (April 2008)
Abstract
We present an efficient method for the reduction of model equations in the linearized diffuse optical tomography (DOT) problem. We first implement the maximum a posteriori (MAP) estimator and Tikhonov regularization, which are based on applying preconditioners to linear perturbation equations. For model reduction, the precondition is split into two parts: the principal components are considered as reduced size preconditioners applied to linear perturbation equations while the less important components are marginalized as noise. Simulation results illustrate that the new proposed method improves the image reconstruction performance and localizes the abnormal section well with a better computational efficiency.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.