AGT-Diff: Anatomy-Guided Multimodal Adaptive Diffusion for Temporally-Aware PET Denoising

Positron emission tomography (PET) is essential in clinical imaging but limited by long scan times and radiation exposure. Enhancing low-dose PET (LDPET) images is vital for reducing patient burden while maintaining diagnostic reliability. Existing denoising methods either ignore anatomical information from CT or over-integrate it, leading to poor generalization and PET signal distortion. We propose AGT-Diff - Anatomy-Guided Multimodal Adaptive Diffusion for temporally-aware PET denoising. AGT-Diff introduces two key modules. The Anatomy-Guided Multimodal Adaptive Fusion (AGMA) module selectively incorporates CT-derived structural priors through learnable soft fusion, preserving PET-specific metabolic patterns. The Progressive Time-Aware Supervision (PTA) module aligns intermediateduration PET scans with diffusion steps via signal-to-noise-ratio scheduling, enabling realistic and duration-adaptive denoising. Extensive experiments on whole-body PET/CT datasets demonstrate that AGT-Diff surpasses state-of-the-art methods in quantitative accuracy and structural fidelity while reducing dependence on paired high-dose data. Its fast inference and adaptive multimodal design make AGT-Diff a practical and generalizable framework for clinical PET enhancement.