As　the　rapid　growth　of　CT　based　medical　application,　low-dose　CT　reconstruction　becomes　more　and　more　important　to　human　health.　Compared　with　other　methods,　statistical　iterative　reconstruction　(SIR)　usually　performs　better　in　low-dose　case.　However,　the　reconstructed　image　quality　of　SIR　highly　depends　on　the　prior　based　regularization　due　to　the　insufficient　of　low-dose　data.　The　frequently-used　regularization　is　developed　from　pixel　based　prior,　such　as　the　smoothness　between　adjacent　pixels.　This　kind　of　pixel　based　constraint　cannot　distinguish　noise　and　structures　effectively.　Recently,　patch　based　methods,　such　as　dictionary　learning　and　non-local　means　filtering,　have　outperformed　the　conventional　pixel　based　methods.　Patch　is　a　small　area　of　image,　which　expresses　structural　information　of　image.　In　this　paper,　we　propose　to　use　patch　based　constraint　to　improve　the　image　quality　of　low-dose　CT　reconstruction.　In　the　SIR　framework,　both　patch　based　sparsity　and　similarity　are　considered　in　the　regularization　term.　On　one　hand,　patch　based　sparsity　is　addressed　by　sparse　representation　and　dictionary　learning　methods,　on　the　other　hand,　patch　based　similarity　is　addressed　by　non-local　means　filtering　method.　We　conducted　a　real　data　experiment　to　evaluate　the　proposed　method.　The　experimental　results　validate　this　method　can　lead　to　better　image　with　less　noise　and　more　detail　than　other　methods　in　low-count　and　few-views　cases.