This　paper　presents　an　effective　and　efficient　dual　domain　network　based　on　Swin　Transformer,　called　SwinDDnet,　for　Metal　Artifact　Reduction　(MAR)　in　CT　or　CBCT　images.　Challenges　in　MAR　lie　in　following　aspects:　first,　MAR　is　very　fastidious　about　pursuing　better　performance　as　a　clinical　application,　but　the　methods　grounded　in　Convolutional　Neural　Network　(CNN)　are　unable　to　it　due　to　the　locality　of　CNN;　second,　although　Transformer　compensates　for　the　defects　of　CNN,　yet　it　can　not　model　at　various　scales　and　deal　with　images　with　high-resolution　pixels;　third,　the　diverse　morphologies　of　metal　artifacts　make　it　thorny　to　solve　the　problem　just　in　the　single　domain.　To　handle　the　issues,　we　present　a　new　transformer-based　approach　to　overcome　metal　artifacts　in　the　dual　domain.　We　adopt　Swin　Transformer　as　the　network　backbone,　capturing　multi-scale　context　information　at　a　global　view　and　reducing　the　quadratic　computation　complexity　down　to　the　linear　one.　Additionally,　this　network　can　abate　heteromorphic　artifacts　in　the　image　and　projection　domains　with　a　concerted　effect.　Further,　we　coalesce　CT　image　priors　generated　by　a　prior　network　to　refine　the　quality　of　final　CT　images.　Finally,　extensive　experiment　results　reveal　that　our　strategy　outperforms　some　other　methods.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.