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Fully Nonlinear SP3 Approximation Based Fluorescence Optical Tomography
Umeå University, Faculty of Science and Technology, Department of Computing Science.
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2017 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 36, no 11, p. 2308-2318Article in journal (Refereed) Published
Abstract [en]

In fluorescence optical tomography, many works in the literature focus on the linear reconstruction problem to obtain the fluorescent yield or the linearized reconstruction problem to obtain the absorption coefficient. The nonlinear reconstruction problem, to reconstruct the fluorophore absorption coefficient, is of interest in imaging studies as it presents the possibility of better reconstructions owing to a more appropriate model. Accurate and computationally efficient forward models are also critical in the reconstruction process. The SPN approximation to the radiative transfer equation (RTE) is gaining importance for tomographic reconstructions owing to its computational advantages over the full RTE while being more accurate and applicable than the commonly used diffusion approximation. This paper presents Gauss-Newton-based fully nonlinear reconstruction for the SP3 approximated fluorescence optical tomography problem with respect to shape as well as the conventional finite-element method-based representations. The contribution of this paper is the Frechet derivative calculations for this problem and demonstration of reconstructions in both representations. For the shape reconstructions, radial-basis-function represented level-set-based shape representations are used. We present reconstructions for tumor-mimicking test objects in scattering and absorption dominant settings, respectively, for moderately noisy data sets in order to demonstrate the viability of the formulation. Comparisons are presented between the nonlinear and linearized reconstruction schemes in an element wise setting to illustrate the benefits of using the former especially for absorption dominant media.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 36, no 11, p. 2308-2318
Keyword [en]
Fluorescence tomography, optical imaging/OCT/DOT, image reconstruction - iterative methods
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-141979DOI: 10.1109/TMI.2017.2718028ISI: 000414134200011PubMedID: 28644802OAI: oai:DiVA.org:umu-141979DiVA, id: diva2:1163246
Available from: 2017-12-06 Created: 2017-12-06 Last updated: 2018-06-09Bibliographically approved

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Eriksson, Jerry

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CiteExportLink to record
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  • apa
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