&lt;title&gt;Image reconstruction for random media by diffusion tomography&lt;/title&gt;

Model, Regine; Huenlich, Rolf; Orlt, Matthias; Walzel, Monika

doi:10.1117/12.209991

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…162 have been reported by several groups. [33][34][35][36][37][38][39] If the voxel dimensions are larger than the diffusion coefficient D1r2-and preferably several times that-and the boundary conditions are properly chosen, the performance of a diffusion-based imaging operator can be qualitatively very similar to that of the transport-based operator. Unfortunately, there is as yet no set of standardized problems on which all groups work, so no detailed quantitative comparison of different approaches to either the forward or the inverse problem is possible.…”

Section: Discussionmentioning

confidence: 99%

Recovery of optical cross-section perturbations in dense-scattering media by transport-theory-based imaging operators and steady-state simulated data

et al. 1996

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We present a useful strategy for imaging perturbations of the macroscopic absorption cross section of dense-scattering media using steady-state light sources. A perturbation model based on transport theory is derived, and the inverse problem is simplified to a system of linear equations, WΔμ = ΔR, where W is the weight matrix, Δμ is a vector of the unknown perturbations, and ΔR is the vector of detector readings. Monte Carlo simulations compute the photon flux across the surfaces of phantoms containing simple or complex inhomogeneities. Calculation of the weight matrix is also based on the results of Monte Carlo simulations. Three reconstruction algorithms-conjugate gradient descent, projection onto convex sets, and the simultaneous algebraic reconstruction technique, with or without imposed positivity constraints-are used for image reconstruction. A rescaling technique that improves the conditioning of the weight matrix is also developed. Results show that the analysis of time-independent data by a perturbation model is capable of resolving the internal structure of a dense-scattering medium. Imposition of positivity constraints improves image quality at the cost of a reduced convergence rate. Use of the rescaling technique increases the initial rate of convergence, resulting in accurate images in a smaller number of iterations.

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Section: Discussionmentioning

confidence: 99%

Recovery of optical cross-section perturbations in dense-scattering media by transport-theory-based imaging operators and steady-state simulated data

et al. 1996

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“…The light propagation in highly scattering media may be described by the widely used diffusion approximation of the Boltzmann transport equation, which leads to the parabolic differential equation for the photon density ' (1) Here c is the speed of light in the medium, pa(X) the absorption coefficient and s(x, t) a source term. The optical diffusion coefficient is…”

Section: Mathematical Modelmentioning

confidence: 99%

“…For completeness, an initial condition (x, 0) = O(x) must be given. The light source may be described in two ways, first by the photon source term s(x, t) in equation(1) and secondly by the initial function 0(x) corresponding to a 5-pulse in time. For short light pulses, as considered here, the results are approximately the same.…”

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confidence: 99%