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

Approximate marginalization of absorption and scattering in fluorescence diffuse optical tomography