Recent advances in optical imaging systems and systemically administered fluorescent probes have significantly improved the ways by which we can visualize proteomics in vivo. A key component in the design of fluorescent probes is a favorable biodistribution, i.e., localization only in the targeted diseased tissue, in order to achieve high contrast and good detection characteristics. In practice, however, there is always some level of background fluorescence present that could result in distorted or obscured visualization and quantification of measured signals. In this study we observe the effects of background fluorescence in tomographic imaging. We demonstrate that increasing levels of background fluorescence result in artifacts when using a linear perturbation algorithm, along with a significant loss of image fidelity and quantification accuracy. To correct for effects of background fluorescence, we have applied cubic polynomial fits to bulk raw measurements obtained from spatially homogeneous and heterogeneous phantoms. We show that subtraction of the average fluorescence response from the raw data before reconstruction can improve image quality and quantification accuracy as shown in relatively homogeneous or heterogeneous phantoms. Subtraction methods thus appear to be a promising route for adaptively correcting nonspecific background fluorochrome distribution.