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We present a reconstruction method involving maximum-likelihood expectationmaximization (MLEM) to model Poisson noise as applied to fluorescence moleculartomography (FMT). MLEM is initialized with the output from a sparsereconstruction-based approach, which performs truncated singular valuedecomposition-based preconditioning followed by fast iterativeshrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivationfor this approach is that sparsity information could be accounted for withinthe initialization, while MLEM would accurately model Poisson noise in the FMTsystem. Simulation experiments show the proposed method significantly improvesimages qualitatively and quantitatively. The method results in over 20 timesfaster convergence compared to uniformly initialized MLEM and improvesrobustness to noise compared to pure sparse reconstruction. We alsotheoretically justify the ability of the proposed approach to reduce noise inthe background region compared to pure sparse reconstruction. Overall, theseresults provide strong evidence to model Poisson noise in FMT reconstructionand for application of the proposed reconstruction framework to FMT imaging.

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization