Approximate marginalization of absorption and scattering in fluorescence diffuse optical tomography

Meghdoot  Mozumder; Tanja Tarvainen; Simon  Arridge; Jari P. Kaipio; Cosimo  D'Andrea; Ville Kolehmainen

doi:10.3934/ipi.2016.10.227

Article Contents

2016, Volume 10, Issue 1: 227-246. Doi: 10.3934/ipi.2016.10.227

This issue Previous Article Preconditioned conjugate gradient method for boundary artifact-free image deblurring Next Article A partial data result for less regular conductivities in admissible geometries

Approximate marginalization of absorption and scattering in fluorescence diffuse optical tomography

1.
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio
2.
Department of Computer Science, University College London, Gower Street, London WC1E 6BT
3.
Department of Mathematics, University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142
4.
Center for Nanoscience and Technology, Istituto Italiano di Tecnologia, Dept of Physics, Politecnico di Milano, 20133 Milan
5.
University of Eastern Finland, Department of Applied Physics, P.O.Box 1627, 70211 Kuopio

Received: October 2014

Revised: March 2015

Published online: February 16, 2016

Abstract / Introduction Related Papers Cited by

Abstract

In fluorescence diffuse optical tomography (fDOT), the reconstruction of the fluorophore concentration inside the target body is usually carried out using a normalized Born approximation model where the measured fluorescent emission data is scaled by measured excitation data. One of the benefits of the model is that it can tolerate inaccuracy in the absorption and scattering distributions that are used in the construction of the forward model to some extent. In this paper, we employ the recently proposed Bayesian approximation error approach to fDOT for compensating for the modeling errors caused by the inaccurately known optical properties of the target in combination with the normalized Born approximation model. The approach is evaluated using a simulated test case with different amount of error in the optical properties. The results show that the Bayesian approximation error approach improves the tolerance of fDOT imaging against modeling errors caused by inaccurately known absorption and scattering of the target.

Keywords:

Image reconstruction techniques,
tomography,
inverse problems,
Bayesian methods,
fluorescence diffuse optical tomography.

Mathematics Subject Classification: Primary: 74J25, 92C55; Secondary: 65R32.

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