Bioluminescence　tomography　(BLT)　provides　fundamental　insight　into　biological　processes　in　vivo.　To　fully　realize　its　potential,　it　is　important　to　develop　image　reconstruction　algorithms　that　accurately　visualize　and　quantify　the　bioluminescence　signals　taking　advantage　of　limited　boundary　measurements.　In　this　study,　a　new　2-step　reconstruction　method　for　BLT　is　developed　by　taking　advantage　of　the　sparse　a　priori　information　of　the　light　emission　using　multispectral　measurements.　The　first　step　infers　a　wavelength-dependent　prior　by　using　all　multi-wavelength　measurements.　The　second　step　reconstructs　the　source　distribution　based　on　this　developed　prior.　Simulation,　phantom　and　in　vivo　results　were　performed　to　assess　and　compare　the　accuracy　and　the　computational　efficiency　of　this　algorithm　with　conventional　sparsity-promoting　BLT　reconstruction　algorithms,　and　results　indicate　that　the　position　errors　are　reduced　from　a　few　millimeters　down　to　submillimeter,　and　reconstruction　time　is　reduced　by　3　orders　of　magnitude　in　most　cases,　to　just　under　a　few　seconds.　The　recovery　of　single　objects　and　multiple　(2　and　3)　small　objects　is　simulated,　and　the　recovery　of　images　of　a　mouse　phantom　and　an　experimental　animal　with　an　existing　luminescent　source　in　the　abdomen　is　demonstrated.　Matlab　code　is　available　at　.　https://github.com/jinchaofeng/code/tree/master.