Rotational　Cherenkov-Excited　Luminescence　Scanned　Tomography　(RCELST)　is　an　emerging　optical　imaging　technology　that　visualizes　the　distribution　of　luminescent　quantum　yield　within　a　treated　subject.　This　technology　involves　collecting　luminescence　signals　resulting　from　the　excitation　of　luminescence　probes　by　Cherenkov　emissions　induced　by　the　rotational　scanning　of　MV　X-rays.　These　signals　are　then　mapped　into　a　sinogram　for　reconstructing　the　distribution　of　luminescent　quantum　yield　by　neural　networks.　Vision　Transformers　(ViTs),　an　effective　deep　learning　algorithm　known　for　capturing　long-distance　dependencies,　have　been　applied　to　medical　image　reconstruction　tasks.　However,　the　large　scale　of　medical　images,　combined　with　the　quadratic　complexity　of　ViTs,　leads　to　erratic　and　time-consuming　reconstruction　performance.　Therefore,　a　more　efficient　algorithm　is　essential　for　reducing　reconstruction　time　while　maintaining　accuracy.　In　this　study,　we　propose　the　Symmetry　Vision　Mamba　(S-VM)　to　address　this　challenge,　reducing　computational　time　while　maintaining　high　reconstruction　accuracy.　The　S-VM　builds　on　the　Vision　Mamba,　which　leverages　State　Space　Models　(SSMs)　to　extract　global　information　from　2D　sinogram　signals.　With　linear　computational　complexity,　S-VM　significantly　accelerates　the　learning　process　compared　to　the　Transformer　algorithms.　Additionally,　S-VM　utilizes　a　symmetrical　encoder　architecture,　incorporating　convolutional　stems　to　extract　local　features　and　enable　multi-scale　feature　fusion　by　sharing　parameters　between　the　two　encoder　branches.　Training　on　10,000　sinogram　signals,　the　S-VM　algorithm　achieves　a　peak　signal-to-noise　ratio　(PSNR)　of　up　to　38.67　dB　and　a　structural　similarity　index　measure　(SSIM)　of　0.97.　Remarkably,　these　results　are　achieved　in　a　floating　point　operations　(FLOPs)　of　4.7G.　©　2025　SPIE.