Much　research　has　been　devoted　to　the　problem　of　low-dose　computerized　tomography　(CT)image　reconstruction　with　a　scan　protocol　by　both　lowering　the　X-ray　tube　current　(low-mAs)and　reducing　the　total　number　of　projection　views(sparse　view).　However,　the　CT　transmission　data　may　be　severely　corrupted　by　X-ray　quanta　noise　and　system　electronic　noise.　Recently,　a　nonlocal　means　(NLM)regularization　method　for　low-dose　CT　reconstruction　was　proposed.　Although　this　method　is　effective　in　suppressing　both　Poisson–Gaussian　noise　and　artifacts,　it　has　two　disadvantages:　the　heavy　computational　burden　and　blurred　edge　information　in　CT　reconstruction.　This　paper　proposes　an　adaptive　patch-wise　regularization　method　for　low-dose　CT　reconstruction　from　available　CT　transmission　data　in　Poisson–Gaussian　noise.　The　proposed　cost　function　includes　a　penalized　weighted　least-square　term　and　two　patch-wise　regularization　terms　which　are　combined　with　a　novel　adaptive　regularization　parameter.　The　two　regularization　terms　take　advantage　of　image　redundant　information　across　different　scales.　By　exploiting　both　global　and　local　structure　information,　the　reconstruction　accuracy　is　enhanced.　In　addition,　we　select　the　adaptive　regularization　parameter　based　on　the　texture　of　the　image　patch　so　that　the　edge　information　is　further　maintained.　Furthermore,　our　algorithm　updates　a　patch　rather　than　a　pixel,　the　computation　burden　is　greatly　reduced.　Finally,　experiment　results　show　that　the　proposed　adaptive　patch-wise　regularization　method　for　low-dose　CT　reconstruction　is　superior　to　several　conventional　regularization-based　approaches　in　terms　of　computation　efficiency　and　resolution　quality.　©　2019　Elsevier　GmbH