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Inverse Problems and Imaging (IPI)
 

Approximate marginalization of absorption and scattering in fluorescence diffuse optical tomography

Pages: 227 - 246, Volume 10, Issue 1, February 2016      doi:10.3934/ipi.2016.10.227

 
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Meghdoot Mozumder - Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland (email)
Tanja Tarvainen - Department of Applied Physics, University of Eastern Finland, P.O.Box 1627, 70211 Kuopio, Finland (email)
Simon Arridge - Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom (email)
Jari P. Kaipio - Department of Mathematics, University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand (email)
Cosimo D'Andrea - Center for Nanoscience and Technology, Istituto Italiano di Tecnologia, Dept of Physics, Politecnico di Milano, 20133 Milan, Italy (email)
Ville Kolehmainen - University of Eastern Finland, Department of Applied Physics, P.O.Box 1627, 70211 Kuopio, Finland (email)

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.

Received: October 2014;      Revised: March 2015;      Available Online: February 16 2016.

 References