Multi-modality　medical　images　can　provide　relevant　or　complementary　information　for　a　target　(organ,　tumor　or　tissue).　Registering　multi-modality　images　to　a　common　space　can　fuse　these　comprehensive　information,　and　bring　convenience　for　clinical　application.　Recently,　neural　networks　have　been　widely　investigated　to　boost　registration　methods.　However,　it　is　still　challenging　to　develop　a　multi-modality　registration　network　due　to　the　lack　of　robust　criteria　for　network　training.　In　this　work,　we　propose　a　multi-modality　registration　network　(MMRegNet),　which　can　perform　registration　between　multi-modality　images.　Meanwhile,　we　present　spatially　encoded　gradient　information　to　train　MMRegNet　in　an　unsupervised　manner.　The　proposed　network　was　evaluated　on　the　public　dataset　from　MM-WHS　2017.　Results　show　that　MMRegNet　can　achieve　promising　performance　for　left　ventricle　registration　tasks.　Meanwhile,　to　demonstrate　the　versatility　of　MMRegNet,　we　further　evaluate　the　method　using　a　liver　dataset　from　CHAOS　2019.　Our　source　code　is　publicly　available　(https://github.com/NanYoMy/mmregnet　).　©　2022,　Springer　Nature　Switzerland　AG.