Sparse algebraic reconstruction for fluorescence mediated tomography

In this paper, we explore the use of anatomical information as a guide in the image formation process of fluorescence molecular tomography (FMT). Namely, anatomical knowledge obtained from high resolution computed tomography (micro-CT) is used to construct a model for the diffusion of light and to constrain the reconstruction to areas candidate to contain fluorescent volumes. Moreover, a sparse regularization term is added to the state-of-the-art least square solution to contribute to the sparsity of the localization. We present results showing the increase in accuracy of the combined system over conventional FMT, for a simulated experiment of lung cancer detection in mice.