An Efficient Method for Model Reduction in Diffuse Optical Tomography
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 m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran University of Science and Technology
2008-01-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/browse.php?a_code=A-10-3-33&slc_lang=en&sid=1 |
| Summary: | 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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| ISSN: | 1735-2827 2383-3890 |