Regularization　methods　have　been　broadly　applied　to　bioluminescence　tomography　(BLT)　to　obtain　stable　solutions,　including　l(2)　and　l(1)　regularizations.　However,　l(2)　regularization　can　oversmooth　reconstructed　images　and　l(1)　regularization　may　sparsify　the　source　distribution,　which　degrades　image　quality.　In　this　paper,　the　use　of　total　variation　(TV)　regularization　in　BLT　is　investigated.　Since　a　nonnegativity　constraint　can　lead　to　improved　image　quality,　the　nonnegative　constraint　should　be　considered　in　BLT.　However,　TV　regularization　with　a　nonnegativity　constraint　is　extremely　difficult　to　solve　due　to　its　nondifferentiability　and　nonlinearity.　The　aim　of　this　work　is　to　validate　the　split　Bregman　method　to　minimize　the　TV　regularization　problem　with　a　nonnegativity　constraint　for　BLT.　The　performance　of　split　Bregman-resolved　TV　(SBRTV)　based　BLT　reconstruction　algorithm　was　verified　with　numerical　and　in　vivo　experiments.　Experimental　results　demonstrate　that　the　SBRTV　regularization　can　provide　better　regularization　quality　over　l(2)　and　l(1)　regularizations.　(c)　2012　Optical　Society　of　America