CT　images　are　commonly　used　in　medical　clinical　diagnosis.　However,　due　to　factors　such　as　hardware　and　scanning　time,　CT　images　in　real　scenes　are　limited　by　spatial　resolution　so　that　doctors　cannot　perform　accurate　disease　analysis　on　tiny　lesion　areas　and　pathological　features.　An　image　super-resolution　(SR)　method　based　on　deep　learning　is　a　good　way　to　solve　this　problem.　Although　many　excellent　networks　have　been　proposed,　but　they　all　pay　more　attention　to　image　quality　indicators　than　image　visual　perception　quality.　Unlike　other　networks　that　focus　more　on　image　evaluation　metrics,　the　super　resolution　generative　adversarial　network　(SRGAN)　has　achieved　tremendous　improvements　in　image　perception　quality.　Based　on　the　above,　this　paper　proposes　a　CT　image　super-resolution　algorithm　based　on　improved　SRGAN.　In　order　to　improve　the　visual　quality　of　CT　images,　a　dilated　convolution　module　is　introduced.　At　the　same　time,　in　order　to　improve　the　overall　visual　effect　of　the　image,　the　mean　structural　similarity　(MSSIM)　loss　is　also　introduced　to　improve　the　perceptual　loss　function.　Experimental　results　on　the　public　CT　image　dataset　demonstrate　that　our　model　is　better　than　the　baseline　method　SRGAN　not　only　in　mean　opinion　score(MOS),　but　also　in　peak　signal-to-noise　ratio　(PSNR)　and　structural　similarity　(SSIM)　values.　©　2020　IEEE.