Reconstruction of optical properties of low-scattering tissue using derivative estimated through perturbation Monte-Carlo method

An iterative method for the reconstruction of optical properties of a low-scattering object, which uses a Monte-Carlo-based forward model, is developed. A quick way to construct and update the Jacobian needed to reconstruct a discretized object, based on the perturbation Monte-Carlo (PMC) approach,...

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Bibliographic Details
Published in:Journal of Biomedical Optics 2004-09, Vol.9 (5), p.1002-1012
Main Authors: Kumar, Y. Phaneendra, Vasu, R. M
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
Connective Tissue - physiology
Connective Tissue - ultrastructure
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
image reconstruction
inverse problems
Models, Biological
Models, Statistical
Monte Carlo Method
Monte-Carlo simulation
Numerical Analysis, Computer-Assisted
optical diagnostics
photon migration
Reproducibility of Results
Scattering, Radiation
Sensitivity and Specificity
Tomography, Optical - methods
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Summary:An iterative method for the reconstruction of optical properties of a low-scattering object, which uses a Monte-Carlo-based forward model, is developed. A quick way to construct and update the Jacobian needed to reconstruct a discretized object, based on the perturbation Monte-Carlo (PMC) approach, is demonstrated. The projection data is handled either one view at a time, using a propagation-backpropagation (PBP) strategy where the dimension of the inverse problem and consequently the computation time are smaller, or, when this approach failed, using all the views simultaneously with a full dataset. The main observations and results are as follows. 1. Whereas the PMC gives an accurate and quick method for constructing the Jacobian the same, when adapted to update the computed projection data, the data are not accurate enough for use in the iterative reconstruction procedure leading to convergence. 2. The assumption of the location of inhomogeneities in the object reduces the dimension of the problem, leading to faster convergence in all the cases considered, such as an object with multiple inhomogeneities and data handled one view at a time (i.e., the PBP approach). 3. On the other hand, without knowledge of the location of inhomogeneities, the problem was too ill posed for the PBP approach to converge to meaningful reconstructions when both absorption and scattering coefficients are considered as unknowns. Finally, to bring out the effectiveness of this method for reconstructing low-scattering objects, we apply a diffusion equation-based algorithm on a dataset from one of the low-scattering objects and show that it fails to reconstruct object inhomogeneities. ©
ISSN:1083-3668
1560-2281
DOI:10.1117/1.1778733