As　a　new　mode　of　molecular　imaging,　bioluminescence　tomography　(BLT)　will　have　signi.cant　e.ect　on　revealing　the　molecular　and　cellular　information　in　vivo　at　the　whole-body　small　animal　level　because　of　its　high　sensitive　detection　and　facile　operation.　However,　BLT　is　an　ill-posed　problem,　it　is　necessary　to　incorporate　a　priori　knowledge　into　the　tomographic　algorithm.　In　this　paper,　a　novel　Bayesian　reconstruction　algorithm　for　BLT　is　firstly　proposed.　In　the　algorithm,　a　priori　permissible　source　region　strategy　is　incorporated　into　the　Bayesian　network　to　reduce　the　ill-posedness　of　BLT.　Then　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　adaptive　finite　element　analysis.　Finally,　the　algorithm　maximizes　the　log　posterior　probability　with　respect　to　a　noise　parameter　and　the　unknown　source　density,　the　distribution　of　bioluminescent　source　can　be　reconstructed.　In　addition,　the　novel　tomography　algorithm　based　adaptive　finite　element　makes　the　method　more　appropriate　for　complex　phantom　such　as　real　mouse.　In　the　numerical　simulation,　a　heterogeneous　phantom　is　used　to　evaluate　the　performance　of　the　proposed　algorithm　with　the　Monte　Carlo　based　synthetic　data.　The　accurate　localization　of　bioluminescent　source　and　quantitative　results　show　the　effectiveness　and　potential　of　the　tomographic　algorithm　for　BLT.　©　2009　SPIE.