Among　many　molecular　imaging　modalities,　Bioluminescence　tomography　(BLT)　is　an　important　optical　molecular　imaging　modality.　Due　to　its　unique　advantages　in　specificity,　sensitivity,　cost-effectiveness　and　low　background　noise,　BLT　is　widely　studied　for　live　small　animal　imaging.　Since　only　the　photon　distribution　over　the　surface　is　measurable　and　the　photo　propagation　with　biological　tissue　is　highly　diffusive,　BLT　is　often　an　ill-posed　problem　and　may　bear　multiple　solutions　and　aberrant　reconstruction　in　the　presence　of　measurement　noise　and　optical　parameter　mismatches.　For　many　BLT　practical　applications,　such　as　early　detection　of　tumors,　the　volumes　of　the　light　sources　are　very　small　compared　with　the　whole　body.　Therefore,　the　L1-norm　sparsity　regularization　has　been　used　to　take　advantage　of　the　sparsity　prior　knowledge　and　alleviate　the　ill-posedness　of　the　problem.　Iterative　shrinkage　(IST)　algorithm　is　an　important　research　achievement　in　a　field　of　compressed　sensing　and　widely　applied　in　sparse　signal　reconstruction.　However,　the　convergence　rate　of　IST　algorithm　depends　heavily　on　the　linear　operator.　When　the　problem　is　ill-posed,　it　becomes　very　slow.　In　this　paper,　we　present　a　sparsity　regularization　reconstruction　method　for　BLT　based　on　the　two-step　iterated　shrinkage　approach.　By　employing　Two-step　strategy　of　iterative　reweighted　shrinkage　(IRS)　to　improve　IST,　the　proposed　method　shows　faster　convergence　rate　and　better　adaptability　for　BLT.　The　simulation　experiments　with　mouse　atlas　were　conducted　to　evaluate　the　performance　of　proposed　method.　By　contrast,　the　proposed　method　can　obtain　the　stable　and　comparable　reconstruction　solution　with　less　number　of　iterations.