Clinically Generalizable Low-Dose CT Denoising for Pediatric Imaging via Enhanced Diffusion Posterior Sampling

In total-body positron emission tomography and computed tomography (PET/CT) imaging, reducing the radiation dose of diagnostic CT scans is essential for minimizing overall radiation exposure, particularly in pediatric patients. Although deep learning-based denoising methods have shown promise in restoring low-dose CT (LDCT) to normal-dose CT (NDCT) quality, most approaches rely on structurally aligned paired data, which are difficult to acquire in clinical practice. Models trained on synthetic pairs often exhibit limited generalizability to real LDCT data. Unconditional diffusion models demonstrate outstanding generalizability, but fail to preserve structural fidelity. To address these challenges, we propose an enhanced diffusion posterior sampling (E-DPS) framework that combines a one-step denoiser U-Net with an unconditional diffusion model. Specifically, the U-Net estimator, trained on simulated LDCT-NDCT pairs, provides preliminary denoised outputs as structural constraints, whereas the diffusion model captures the prior distribution of NDCT images to enhance realism and generalizability. During inference, the U-Net predictions are integrated as constraints with tunable weights, thereby guiding diffusion posterior sampling. In addition, an intermediate-stage initialization strategy is introduced, significantly reducing the number of required sampling steps. Extensive experiments on simulated LDCT datasets across three dose levels demonstrate the superiority of our method, yielding average PSNR gains of +5.2% and +4.3% at unseen dose levels compared with state-of-the-art approaches. Moreover, on real LDCT images, E-DPS exhibits strong zero-shot generalizability, achieving better noise suppression while preserving anatomical detail. These results highlight the robustness and clinical potential of E-DPS for LDCT denoising.