PurposeThis　study　aims　to　develop　and　validate　a　deep　learning　framework　designed　to　eliminate　the　second　CT　scan　of　dual-tracer　total-body　PET/CT　imaging.MethodsWe　retrospectively　included　three　cohorts　of　247　patients　who　underwent　dual-tracer　total-body　PET/CT　imaging　on　two　separate　days　(time　interval:1-11　days).　Out　of　these,　167　underwent　[68Ga]Ga-DOTATATE/[18F]FDG,　50　underwent　[68Ga]Ga-PSMA-11/[18F]FDG,　and　30　underwent　[68Ga]Ga-FAPI-04/[18F]FDG.　A　deep　learning　framework　was　developed　that　integrates　a　registration　generative　adversarial　network　(RegGAN)　with　non-rigid　registration　techniques.　This　approach　allows　for　the　transformation　of　attenuation-correction　CT　(ACCT)　images　from　the　first　scan　into　pseudo-ACCT　images　for　the　second　scan,　which　are　then　used　for　attenuation　and　scatter　correction　(ASC)　of　the　second　tracer　PET　images.　Additionally,　the　derived　registration　transform　facilitates　dual-tracer　image　fusion　and　analysis.　The　deep　learning-based　ASC　PET　images　were　evaluated　using　quantitative　metrics,　including　mean　absolute　error　(MAE),　peak　signal-to-noise　ratio　(PSNR),　and　structural　similarity　index　measure　(SSIM)　across　the　whole　body　and　specific　regions.　Furthermore,　the　quantitative　accuracy　of　PET　images　was　assessed　by　calculating　standardized　uptake　value　(SUV)　bias　in　normal　organs　and　lesions.ResultsThe　MAE　for　whole-body　pseudo-ACCT　images　ranged　from　97.64　to　112.59　HU　across　four　tracers.　The　deep　learning-based　ASC　PET　images　demonstrated　high　similarity　to　the　ground-truth　PET　images.　The　MAE　of　SUV　for　whole-body　PET　images　was　0.06　for　[68Ga]Ga-DOTATATE,　0.08　for　[68Ga]Ga-PSMA-11,　0.06　for　[68Ga]Ga-FAPI-04,　and　0.05　for　[18F]FDG,　respectively.　Additionally,　the　median　absolute　percent　deviation　of　SUV　was　less　than　2.6%　for　all　normal　organs,　while　the　mean　absolute　percent　deviation　of　SUV　was　less　than　3.6%　for　lesions　across　four　tracers.ConclusionThe　proposed　deep　learning　framework,　combining　RegGAN　and　non-rigid　registration,　shows　promise　in　reducing　CT　radiation　dose　for　dual-tracer　total-body　PET/CT　imaging,　with　successful　validation　across　multiple　tracers.