Cherenkov-excited　luminescence　scanned　tomography　(CELST)　is　a　new　emerging　imaging　modality,　which　uses　the　Cherenkov　light　to　excite　fluorophores　for　tomographic　imaging.　In　order　to　improve　the　imaging　depth　and　spatial　resolution,　a　rotational　CELST　was　developed　to　scan　the　imaging　object　to　produce　sinogram　data,　and　a　filtered　back　projection　(FBP)　was　used　to　recover　the　distribution　of　fluorophores.　However,　the　images　reconstructed　by　FBP　are　usually　corrupted　by　artifacts　due　to　measurements　from　limited　angles.　To　reduce　the　artifacts,　we　propose　a　deep　learning-based　reconstruction　algorithm　(SAM-Unet),　which　is　based　on　a　fully　convolutional　deep　neural　network　with　U-Net　structure,　and　a　spatial　attention　module　was　added　between　the　encoder　and　the　decoder.　The　image　features　extracted　by　the　spatial　attention　module　are　transferred　to　the　decoder　through　a　skip　connection　structure.　The　effectiveness　of　the　proposed　SAM-Unet　is　verified　by　numerical　experiments,　and　the　results　show　that　the　SAM-Unet　can　improve　the　mean　square　error　(MSE)　(97.5%),　peak　signal-To-noise　ratio　(PSNR)　(81.9%)　and　structure　similarity　index　measure　(SSIM)　(63.4%)　compared　with　the　FBP　algorithm.　Compared　with　the　deep　learning　method　U-Net,　the　MSE　improved　39.8%,　the　PSNR　improved　8.0%　and　SSIM　improved　2.6%.　　©　2023　SPIE.