Abstract
In fluorescence diffuse optical tomography (fDOT), the reconstruction of the fluorophoreconcentration inside the target body is usually carried out using a normalized Born approximation model wherethe 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 themodeling 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 withdifferent amount of error in the optical properties. The results show that the Bayesian approximation error approach improvesthe tolerance of fDOT imaging against modeling errors caused by inaccurately known absorption and scattering of the target.
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