用解剖与频率信息引导扩散模型，提升低剂量PET图像质量。
AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
- 引入解剖-频率双引导机制，增强特征调制与频域一致性。
- 在四个数据集上显著优于传统CNN、GAN、Transformer和扩散模型方法。
- 适合医学影像降噪研究者与放射科医生参考使用。
正电子发射断层扫描（PET）为疾病评估提供关键功能信息，但降低注射剂量或采集时间会导致低剂量（LD）PET图像噪声增强且摄取量化不可靠。扩散模型通过从低剂量输入逐步恢复高剂量（HD）图像，为PET去噪提供了新思路。然而，由于缺乏解剖引导、多尺度特征传播不稳定以及频域摄取恢复不确定，仍面临挑战。本文提出AnF-DiffPET，一种基于CT条件的解剖-频率引导扩散框架，集成解剖-频率引导（AFG）、多尺度交叉变换器重建（MSCTR）和频域对比硬样本挖掘（FCHM），以增强去噪过程中的解剖感知特征调制与频域一致性。在四个PET/CT数据集上的实验表明，该方法在图像保真度、解剖一致性及定量精度方面均优于代表性基于CNN、GAN、Transformer和扩散模型的方法。代码与训练模型将在论文接收后公开。
原文摘要 · Abstract (English)
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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