For　intravascular　ultrasound　(IVUS)　images,　the　registration　technology　is　used　to　calculate　coronary　arteries　displacement　to　analyze　vascular　elasticity.　It　not　only　provides　evidence　for　the　prevention　and　treatment　of　cardiovascular　diseases,　but　also　has　important　significance　for　guiding　interventional　surgery　and　monitoring　the　placement　of　surgical　stents.　Aiming　at　the　high　computational　cost　of　current　traditional　registration　methods　and　the　insufficient　accuracy　of　common　deep　learning　registration　methods　for　IVUS　images,　this　paper　proposes　a　fast　unsupervised　registration　of　IVUS　images　combined　with　an　attention　mechanism.　The　proposed　method　directly　learns　to　estimate　a　displacement　vector　field　(DVF)　from　a　pair　of　input　images　of　the　training　set.　The　spatial　transform　network　(STN)　uses　the　DVF　to　transform　the　moving　image　into　the　fixed　image.　Finally,　the　model　is　trained　by　minimizing　a　similarity　metric　loss　function　between　the　deformed　moving　image　and　the　fixed　image.　Compared　with　the　previous　deep　learning　method,　the　registration　performance　improved　after　implementing　the　proposed　method.　The　proposed　method　can　accurately　register　the　inner　and　outer　membranes　of　IVU　S　images　and　provide　a　reliable　basis　for　vascular　elasticity　analysis.　©　Published　under　licence　by　IOP　Publishing　Ltd.