Magnetic　Resonance　Imaging　(MRI)　generates　medical　images　of　multiple　sequences,　i.e.,　multimodal,　from　different　contrasts.　However,　noise　will　reduce　the　quality　of　MR　images,　and　then　affect　the　doctor＇s　diagnosis　of　diseases.　Existing　filtering　methods,　transform-domain　methods,　statistical　methods　and　Convolutional　Neural　Network　(CNN)　methods　main　aim　to　denoise　individual　sequences　of　images　without　considering　the　relationships　between　multiple　different　sequences.　They　cannot　balance　the　extraction　of　high-dimensional　and　low-dimensional　features　in　MR　images,　and　hard　to　maintain　a　good　balance　between　preserving　image　texture　details　and　denoising　strength.　To　overcome　these　challenges,　this　work　proposes　a　controllable　Multimodal　Cross-Global　Learnable　Attention　Network　(MMCGLANet)　for　MR　image　denoising　with　Arbitrary　Modal　Missing.　Specifically,　Encoder　is　employed　to　extract　the　shallow　features　of　the　image　which　share　weight　module,　and　Convolutional　Long　Short-Term　Memory(ConvLSTM)　is　employed　to　extract　the　associated　features　between　different　frames　within　the　same　modal.　Cross　Global　Learnable　Attention　Network(CGLANet)　is　employed　to　extract　and　fuse　image　features　between　multimodal　and　within　the　same　modality.　In　addition,　sequence　code　is　employed　to　label　missing　modalities,　which　allows　for　Arbitrary　Modal　Missing　during　model　training,　validation,　and　testing.　Experimental　results　demonstrate　that　our　method　has　achieved　good　denoising　results　on　different　public　and　real　MR　image　dataset.