Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

Purpose: This 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.

Methods: We 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 [Ga]Ga-DOTATATE/[F]FDG, 50 underwent [Ga]Ga-PSMA-11/[F]FDG, and 30 underwent [Ga]Ga-FAPI-04/[F]FDG.