Low-dose Positron Emission Tomography (PET) imaging reduces patient radiation exposure but suffers from increased noise that degrades image quality and diagnostic reliability. Although diffusion models have demonstrated strong denoising capability, their stochastic nature makes it challenging to enforce anatomically consistent structures, particularly in low signal-to-noise regimes and volumetric whole-body imaging. We propose Wavelet-Conditioned ControlNet (WCC-Net), a fully 3D diffusion-based framework that introduces explicit frequency-domain structural priors via wavelet representations to guide volumetric PET denoising. By injecting wavelet-based structural guidance into a frozen pretrained diffusion backbone through a lightweight control branch, WCC-Net decouples anatomical structure from noise while preserving generative expressiveness and 3D structural continuity. Extensive experiments demonstrate that WCC-Net consistently outperforms CNN-, GAN-, and diffusion-based baselines. On the internal 1/20-dose test set, WCC-Net improves PSNR by +1.21 dB and SSIM by +0.008 over a strong diffusion baseline, while reducing structural distortion (GMSD) and intensity error (NMAE). Moreover, WCC-Net generalizes robustly to unseen dose levels (1/50 and 1/4), achieving superior quantitative performance and improved volumetric anatomical consistency.

翻译：低剂量正电子发射断层扫描（PET）成像可减少患者辐射暴露，但其图像噪声增加会降低图像质量与诊断可靠性。尽管扩散模型已展现出强大的去噪能力，其随机性使得在低信噪比场景及全身三维成像中难以强制保持解剖结构一致性。本文提出小波条件控制网络（WCC-Net），一种完全基于三维扩散的框架，通过小波表示引入显式的频域结构先验以指导三维PET去噪。通过轻量级控制分支将基于小波的结构引导注入冻结的预训练扩散主干网络，WCC-Net在保持生成表达能力与三维结构连续性的同时，实现了解剖结构与噪声的解耦。大量实验表明，WCC-Net在各项指标上均优于基于CNN、GAN及扩散的基线方法。在内部1/20剂量测试集上，相较于强扩散基线，WCC-Net将PSNR提升了+1.21 dB，SSIM提升了+0.008，同时降低了结构失真（GMSD）与强度误差（NMAE）。此外，WCC-Net对未见剂量水平（1/50与1/4）展现出鲁棒的泛化能力，取得了更优的量化性能并提升了三维解剖一致性。