Cherenkov-excited　luminescence　scanned　tomography　(CELST)　is　an　emerging　imaging　technique　and　its　potential　applications　during　radiation　therapy　have　just　recently　been　explored.　The　aim　of　CELST　is　to　recover　the　distribution　of　luminescent　probes　from　emission　photons.　However,　CELST　images　tend　to　suffer　from　low　resolution　and　degraded　image　quality　due　to　light　multiple　scattering　and　limited　boundary　measurements.　Therefore,　inaccurate　information　about　the　status　of　the　luminescent　probe　is　provided.　To　accurately　capture　the　sparsity　characterization　of　a　luminescent　probe　and　achieve　the　high-quality　image,　a　novel　reconstruction　method,　to　our　knowledge,　is　proposed　for　CELST　by　combining　a　sparse　prior　with　an　attention　network,　termed　LKSVD-Net.　A　multiscale　learned　KSVD　is　first　incorporated　to　obtain　the　local　sparsity　information　of　a　luminescent　probe.　Subsequently,　a　prior　attention　network　is　designed　to　leverage　the　prior　features　related　to　the　measurements.　The　multiscale　sparsity　and　prior　features　are　finally　combined　to　complete　the　image　reconstruction.　Experimental　results　demonstrate　that　the　LKSVD-Net　can　notably　enhance　image　quality　even　in　a　20　dB　signal-to-noise　ratio　(SNR).　Furthermore,　the　proposed　LKSVD-Net　yields　improved　quantitative　accuracy　for　4　mm　diameter　probes　with　an　edge-to-edge　distance　of　2　mm.　The　results　demonstrate　that　LKSVD-Net　improves　the　peak　signal-to-noise　ratio　(PSNR)　by　approximately　15.1%,　structural　similarity　index　measure　(SSIM)　by　about　95.8%,　and　Pearson　correlation　(PC)　by　around　3%　compared　to　Tikhonov　regularization.　©　2025　Optica　Publishing　Group.　All　rights,　including　for　text　and　data　mining　(TDM),　Artificial　Intelligence　(AI)　training,　and　similar　technologies,　are　reserved.