A　medical　image　reconstruction　algorithm　of　CT　images　of　interested　region　with　low　does　based　on　an　L1　norm　dictionary　sparse　constraint　is　proposed　to　address　the　problem　that　the　existing　total　variation　(TV)　regularization　algorithms　often　suffer　from　patchy　artifacts　and　losing　fine　structure.　First,　ROI　image　reconstruction　is　converted　into　an　optimization　problem　by　using　a　penalized　weighted　least　squares　function　to　establish　a　data-fitting　term　and　the　L1　norm　of　sparse　representation　in　terms　of　learned　dictionary　as　a　constraint　term.　Then,　the　objective　function　is　split　into　an　image　updating　sub-optimization　problem　and　a　sparse　representation　sub-optimization　problem,　and　these　two　sub-problems　are　alternatively　solved　in　a　minimization　manner.　Chest　simulation　results　and　a　comparison　to　the　reconstruction　algorithm　with　TV　regularization　show　that　in　the　cases　of　Poisson　noise　projection　added　1×105,　5×104　and　1×104　photons　per　detector　element,　respectively,　the　proposed　algorithm　decreases　the　structural　similarity　index　metric　by　0.103　5,　0.113　1　and　0.125　8,　respectively,　and　improves　the　peak　signal　to　noise　by　4.88,　4.93　and　5.44　dB,　respectivtly.　Moreover,　experiments　with　sheep　Lung　real　CT　validates　that　the　proposed　algorithm　can　effectively　remove　blocky　artifacts　and　preserve　low-contrast　structures.　©　2019,　Editorial　Office　of　Journal　of　Xi＇an　Jiaotong　University.　All　right　reserved.