High-quality　Computed　Tomography(CT)　plays　a　vital　role　in　clinical　diagnosis,　but　the　presence　of　metallic　implants　will　introduce　severe　metal　artifacts　on　CT　images　and　obstruct　doctors＇　decision-making.　Many　prior　researches　on　Metal　Artifact　Reduction(MAR)　are　based　on　Convolutional　Neural　Network(CNN).　Recently,　Transformer　has　demonstrated　phenomenal　potential　in　computer　vision.　Also,　transformer-based　methods　have　been　harnessed　in　CT　image　denoising.　Nevertheless,　these　methods　have　been　little　explored　in　MAR.　To　fill　the　gap,　we　put　forth,　to　the　best　of　our　knowledge,　the　first　transformer-based　architecture　for　MAR.　Our　method　relies　on　a　standard　Vision　Transformer(ViT).　Furthermore,　we　tap　into　the　progressive　tokenization　to　refrain　from　the　simple　tokenization　of　ViT　which　gives　rise　to　inability　to　model　the　local　anatomical　information.　Additionally,　for　the　sake　of　facilitating　the　interaction　among　tokens,　we　take　advantage　of　cyclic　shift　from　Swin　Transformer.　Finally,　many　experiment　results　reveal　that　the　transformer-based　technique　is　superior　to　those　on　the　basis　of　CNN　to　some　degree.　©　2023　IEEE.