As　an　emerging　imaging　technique,　Cherenkov-excited　luminescence　scanned　tomography　(CELST)　can　recover　a　high-resolution　3D　distribution　of　quantum　emission　fields　within　tissue　using　X-ray　excitation　for　deep　penetrance.　However,　its　reconstruction　is　an　ill-posed　and　under-conditioned　inverse　problem　because　of　the　diffuse　optical　emission　signal.　Deep　learning　based　image　reconstruction　has　shown　very　good　potential　for　solving　these　types　of　problems,　however　they　suffer　from　a　lack　of　ground-truth　image　data　to　confirm　when　used　with　experimental　data.　To　overcome　this,　a　self-supervised　network　cascaded　by　a　3D　reconstruction　network　and　the　forward　model,　termed　Selfrec-Net,　was　proposed　to　perform　CELST　reconstruction.　Under　this　framework,　the　boundary　measurements　are　input　to　the　network　to　reconstruct　the　distribution　of　the　quantum　field　and　the　predicted　measurements　are　subsequently　obtained　by　feeding　the　reconstructed　result　to　the　forward　model.　The　network　was　trained　by　minimizing　the　loss　between　the　input　measurements　and　the　predicted　measurements　rather　than　the　reconstructed　distributions　and　the　corresponding　ground　truths.　Comparative　experiments　were　carried　out　on　both　numerical　simulations　and　physical　phantoms.　For　singular　luminescent　targets,　the　results　demonstrate　the　effectiveness　and　robustness　of　the　proposed　network,　and　comparable　performance　can　be　attained　to　a　state-of-the-art　deep　supervised　learning　algorithm,　where　the　accuracy　of　the　emission　yield　and　localization　of　the　objects　was　far　superior　to　iterative　reconstruction　methods.　Reconstruction　of　multiple　objects　is　still　reasonable　with　high　localization　accuracy,　although　with　limits　to　the　emission　yield　accuracy　as　the　distribution　becomes　more　complex.　Overall　though　the　reconstruction　of　Selfrec-Net　provides　a　self-supervised　way　to　recover　the　location　and　emission　yield　of　molecular　distributions　in　murine　model　tissues.　©　2023　Optica　Publishing　Group　under　the　terms　of　the　Optica　Open　Access　Publishing　Agreement.