Significance:　X-ray　Cherenkov-luminescence　tomography　(XCLT)　produces　fast　emission　data　from　megavoltage　(MV)　x-ray　scanning,　in　which　the　excitation　location　of　molecules　within　tissue　is　reconstructed.　However　standard　filtered　backprojection　(FBP)　algorithms　for　XCLT　sinogram　reconstruction　can　suffer　from　insufficient　data　due　to　dose　limitations,　so　there　are　limits　in　the　reconstruction　quality　with　some　artifacts.　We　report　a　deep　learning　algorithm　for　XCLT　with　high　image　quality　and　improved　quantitative　accuracy.Aim:　To　directly　reconstruct　the　distribution　of　emission　quantum　yield　for　x-ray　Cherenkov　luminescence　tomography,　we　proposed　a　three-component　deep　learning　algorithm　that　includes　a　Swin　transformer,　convolution　neural　network,　and　locality　module　model.Approach:　A　data-to-image　model　x-ray　Cherenkov-luminescence　tomography　is　developed　based　on　a　Swin　transformer,　which　is　used　to　extract　pixel-level　prior　information　from　the　sinogram　domain.　Meanwhile,　a　convolutional　neural　network　structure　is　deployed　to　transform　the　extracted　pixel　information　from　the　sinogram　domain　to　the　image　domain.　Finally,　a　locality　module　is　designed　between　the　encoder　and　decoder　connection　structures　for　delivering　features.　Its　performance　was　validated　with　simulation,　physical　phantom,　and　in　vivo　experiments.Results:　This　approach　can　better　deal　with　the　limits　to　data　than　conventional　FBP　methods.　The　method　was　validated　with　numerical　and　physical　phantom　experiments,　with　results　showing　that　it　improved　the　reconstruction　performance　mean　square　error　(＞　94.1%),　peak　signal-tonoise　ratio　(＞　41.7%),　and　Pearson　correlation　(＞　19%)　compared　with　the　FBP　algorithm.　The　Swin-CNN　also　achieved　a　32.1%　improvement　in　PSNR　over　the　deep　learning　method　AUTOMAP.Conclusions:　This　study　shows　that　the　three-component　deep　learning　algorithm　provides　an　effective　reconstruction　method　for　x-ray　Cherenkov-luminescence　tomography.(c)　The　Authors.　Published　by　SPIE　under　a　Creative　Commons　Attribution　4.0　International　License.　Distribution　or　reproduction　of　this　work　in　whole　or　in　part　requires　full　attribution　of　the　original　publication,　including　its　DOI.　[DOI:　10.1117/1.JBO.28.2.026004]