Magnetic　resonance　imaging　(MRI)　is　a　revolutionary　tool　in　medical　imaging,　which　plays　an　important　role　in　clinical　diagnosis.　Compressive　sensing　(CS)　has　shown　great　potential　in　significantly　reducing　the　acquisition　time　of　MRI　scanning.　However,　how　to　improve　the　reconstruction　quality　with　limited　k-space　data　is　still　a　challenge.　MRI　images　are　featured　with　large　area　of　smooth　regions,　sharp　edges　and　rich　textures.　Motivated　by　these　facts,　we　propose　a　nonlocal　autoregressive　model　(NAM)　for　CS　MRI　reconstruction.　Nonlocal　similarity　between　image　patches　is　exploited　as　a　regularization　term　to　constrain　the　nonlocal　feature　in　MRI　images,　which　is　very　helpful　in　preserving　edge　sharpness.　While　an　autoregressive　regularization　term　is　employed　to　describe　the　linear　correlation　between　neighboring　pixels,　which　preserves　more　spatial　details.　Different　from　previous　work,　we　reconstruct　an　MRI　image　patch　utilizing　correlations　both　among　patches　and　among　neighboring　pixels.　Extensive　experimental　results　demonstrate　that　our　method　outperforms　mainstream　methods　in　MRI　reconstruction　in　terms　of　both　subjective　quality　and　objective　quality.