Although　diagnostic　medical　imaging　provides　enormous　benefits　in　the　early　detection　and　accuracy　diagnosis　of　various　diseases,　there　are　growing　concerns　on　the　potential　side　effect　of　radiation　induced　genetic,　cancerous　and　other　diseases.　How　to　reduce　radiation　dose　while　maintaining　the　diagnostic　performance　is　a　major　challenge　in　the　computed　tomography　(CT)　field.　Inspired　by　the　compressive　sensing　theory,　the　sparse　constraint　in　terms　of　total　variation　(TV)　minimization　has　already　led　to　promising　results　for　low-dose　CT　reconstruction.　Compared　to　the　discrete　gradient　transform　used　in　the　TV　method,　dictionary　learning　is　proven　to　be　an　effective　way　for　sparse　representation.　On　the　other　hand,　it　is　important　to　consider　the　statistical　property　of　projection　data　in　the　low-dose　CT　case.　Recently,　we　have　developed　a　dictionary　learning　based　approach　for　low-dose　X-ray　CT.　In　this　paper,　we　present　this　method　in　detail　and　evaluate　it　in　experiments.　In　our　method,　the　sparse　constraint　in　terms　of　a　redundant　dictionary　is　incorporated　into　an　objective　function　in　a　statistical　iterative　reconstruction　framework.　The　dictionary　can　be　either　predetermined　before　an　image　reconstruction　task　or　adaptively　defined　during　the　reconstruction　process.　An　alternating　minimization　scheme　is　developed　to　minimize　the　objective　function.　Our　approach　is　evaluated　with　low-dose　X-ray　projections　collected　in　animal　and　human　CT　studies,　and　the　improvement　associated　with　dictionary　learning　is　quantified　relative　to　filtered　backprojection　and　TV-based　reconstructions.　The　results　show　that　the　proposed　approach　might　produce　better　images　with　lower　noise　and　more　detailed　structural　features　in　our　selected　cases.　However,　there　is　no　proof　that　this　is　true　for　all　kinds　of　structures.