Abstract
Abstract
Objective: Positron emission tomography (PET) provides essential functional information for disease assessment. However, reducing injected activity or acquisition time produces low-dose (LD) PET with stronger count-dependent noise and less reliable uptake quantification. Diffusion models offer a promising solution for PET denoising by progressively recovering high-dose (HD) PET images from LD inputs. However, LD-to-HD PET denoising remains challenging because of limited anatomical guidance, unstable multi-scale feature propagation, and inaccurate recovery of uptake patterns in the frequency domain.
Approach: We propose AnF-DiffPET, an anatomy- and frequency-guided diffusion framework for computed tomography (CT)-conditioned LD PET denoising. The framework integrates Anatomical-Frequency Guidance (AFG), Multi-Scale Cross-Transformer Reconstruction (MSCTR), and Frequency-Contrastive Hard Mining (FCHM) to enhance anatomy-aware feature modulation and frequency-domain consistency during denoising.
Main results: Experimental results across four simulated low-dose PET/CT datasets show that the proposed method improves image fidelity, anatomical consistency, and quantitative fidelity over representative CNN-based, GAN-based, Transformer-based, Mamba-based, and diffusion-based methods.
Significance: AnF-DiffPET provides a PET/CT-specific diffusion restoration framework that combines anatomical guidance, multi-scale feature reconstruction, and frequency-domain regularization for LD PET denoising. The code and trained models will be publicly released upon acceptance.