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Computed Tomography (CT) and Positron Emission Tomography (PET) provide complementary anatomical and metabolic information essential for oncology assessment; however, PET imaging remains constrained by radiation exposure, acquisition cost, and limited accessibility. This work proposes a 3D CT-to-PET translation framework that integrates contrastive latent representation learning within a variational autoencoder (VAE) and Brownian Bridge diffusion to effectively bridge structural and functional modality differences. Evaluated on 1,014 paired PET/CT volumes across five anatomical regions-brain, lung, liver, kidney, and stomach-the proposed approach demonstrates improved synthetic PET signal fidelity and reliable lesion representation while maintaining efficient diffusion inference, supporting diagnostically meaningful virtual PET imaging applications.