AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising

• URL: http://arxiv.org/abs/2607.00509v1
• Date: Wed, 01 Jul 2026 06:44:10 GMT
• Title: AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
• Abstract summary: AnF-DiffPET is an anatomy- and frequency-guided diffusion framework for computed tomography (CT) conditioned LD PET denoising.<n>The proposed method improves image fidelity, anatomical consistency, and quantitative fidelity over representative CNN-based, GAN-based, transformer-based, and diffusion-based methods.
• Score: 4.44890955575059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 is still challenging due to insufficient anatomical guidance, unstable multi-scale feature propagation, and uncertain frequency domain uptake recovery. 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. Experimental results across four PET/CT datasets show that the proposed method improves image fidelity, anatomical consistency, and quantitative fidelity over representative CNN-based, GAN-based, transformer-based, and diffusion-based methods. The code and trained models will be publicly released upon acceptance.

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