Purpose:　Bioluminescence　tomography　(BLT)　provides　an　effective　tool　for　monitoring　physiological　and　pathological　activities　in　vivo.　However,　the　measured　data　in　bioluminescence　imaging　are　corrupted　by　noise.　Therefore,　regularization　methods　are　commonly　used　to　find　a　regularized　solution.　Nevertheless,　for　the　quality　of　the　reconstructed　bioluminescent　source　obtained　by　regularization　methods,　the　choice　of　the　regularization　parameters　is　crucial.　To　date,　the　selection　of　regularization　parameters　remains　challenging.　With　regards　to　the　above　problems,　the　authors　proposed　a　BLT　reconstruction　algorithm　with　an　adaptive　parameter　choice　rule.　Methods:　The　proposed　reconstruction　algorithm　uses　a　diffusion　equation　for　modeling　the　bioluminescent　photon　transport.　The　diffusion　equation　is　solved　with　a　finite　element　method.　Computed　tomography　(CT)　images　provide　anatomical　information　regarding　the　geometry　of　the　small　animal　and　its　internal　organs.　To　reduce　the　ill-posedness　of　BLT,　spectral　information　and　the　optimal　permissible　source　region　are　employed.　Then,　the　relationship　between　the　unknown　source　distribution　and　multiview　and　multispectral　boundary　measurements　is　established　based　on　the　finite　element　method　and　the　optimal　permissible　source　region.　Since　the　measured　data　are　noisy,　the　BLT　reconstruction　is　formulated　as　l(2)　data　fidelity　and　a　general　regularization　term.　When　choosing　the　regularization　parameters　for　BLT,　an　efficient　model　function　approach　is　proposed,　which　does　not　require　knowledge　of　the　noise　level.　This　approach　only　requests　the　computation　of　the　residual　and　regularized　solution　norm.　With　this　knowledge,　we　construct　the　model　function　to　approximate　the　objective　function,　and　the　regularization　parameter　is　updated　iteratively.　Results:　First,　the　micro-CT　based　mouse　phantom　was　used　for　simulation　verification.　Simulation　experiments　were　used　to　illustrate　why　multispectral　data　were　used　rather　than　monochromatic　data.　Furthermore,　the　study　conducted　using　an　adaptive　regularization　parameter　demonstrated　our　ability　to　accurately　localize　the　bioluminescent　source.　With　the　adaptively　estimated　regularization　parameter,　the　reconstructed　center　position　of　the　source　was　(20.37,　31.05,　12.95)　mm,　and　the　distance　to　the　real　source　was　0.63　mm.　The　results　of　the　dual-source　experiments　further　showed　that　our　algorithm　could　localize　the　bioluminescent　sources　accurately.　The　authors　then　presented　experimental　evidence　that　the　proposed　algorithm　exhibited　its　calculated　efficiency　over　the　heuristic　method.　The　effectiveness　of　the　new　algorithm　was　also　confirmed　by　comparing　it　with　the　L-curve　method.　Furthermore,　various　initial　speculations　regarding　the　regularization　parameter　were　used　to　illustrate　the　convergence　of　our　algorithm.　Finally,　in　vivo　mouse　experiment　further　illustrates　the　effectiveness　of　the　proposed　algorithm.　Conclusions:　Utilizing　numerical,　physical　phantom　and　in　vivo　examples,　we　demonstrated　that　the　bioluminescent　sources　could　be　reconstructed　accurately　with　automatic　regularization　parameters.　The　proposed　algorithm　exhibited　superior　performance　than　both　the　heuristic　regularization　parameter　choice　method　and　L-curve　method　based　on　the　computational　speed　and　localization　error.　(C)　2011　American　Association　of　Physicists　in　Medicine.　[DOI:　10.1118/1.3635221]