Bioluminescence　tomography　(BLT)　poses　a　typical　ill-posed　inverse　problem　with　a　large　number　of　unknowns　and　a　relatively　limited　number　of　boundary　measurements.　It　is　indispensable　to　incorporate　a　priori　information　into　the　inverse　problem　formulation　in　order　to　obtain　viable　solutions.　In　the　paper,　Bayesian　approach　has　been　firstly　suggested　to　incorporate　multiple　types　of　a　priori　information　for　BLT　reconstruction.　Meanwhile,　a　generalized　adaptive　Gaussian　Markov　random　field　(GAGMRF)　prior　model　for　unknown　source　density　estimation　is　developed　to　further　reduce　the　ill-posedness　of　BLT　on　the　basis　of　finite　element　analysis.　Then　the　distribution　of　bioluminescent　source　can　be　acquired　by　maximizing　the　log　posterior　probability　with　respect　to　a　noise　parameter　and　the　unknown　source　density.　Furthermore,　the　use　of　finite　element　method　makes　the　algorithm　appropriate　for　complex　heterogeneous　phantom.　The　algorithm　was　validated　by　numerical　simulation　of　a　3-D　micro-CT　mouse　atlas　and　physical　phantom　experiment.　The　reconstructed　results　suggest　that　we　are　able　to　achieve　high　computational　efficiency　and　accurate　localization　of　bioluminescent　source.　(C)　2009　Optical　Society　of　America