UniPET: a unified approach for whole-body CT to PET translation

Positron emission tomography (PET) provides critical metabolic information for oncological imaging, yet its use is constrained by radiation exposure, cost, and limited availability. Synthesizing PET-like images from computed tomography (CT) has been proposed as a way to approximate metabolic information; however, existing approaches either fail to capture region-specific variability or rely on multiple organ-specific models that do not scale to whole-body imaging. In this work, we introduce UniPET, a unified approach for whole-body CT-to-PET translation based on curriculum learning. By progressively incorporating anatomical regions of increasing complexity, UniPET enables a single network to learn coherent morpho-metabolic relationships across different regions. We evaluate UniPET on a public dataset of 900 patients and an external cohort of 579 patients. The model achieves image quality and metabolic consistency comparable to region-specific approaches, while improving over conventional whole-body models and maintaining stable performance under distribution shifts. By capturing region-specific variability within a single model, UniPET provides a promising approach for augmenting CT-based workflows with synthetic metabolic information, with potential applications in screening triage, in low-resource environments where PET is unavailable, and as an additional input for multimodal analysis pipelines. The code is available at: https://github.com/arco-group/UniPET.