用自监督深度去噪器提升低剂量CT图像质量，减少辐射伤害。

Ultra-low-dose CT (ULDCT) imaging can greatly reduce patient radiation exposure, but the resulting scans suffer from severe structured and random noise that degrades image quality. To address this challenge, we propose a novel Plug-and-Play model-based iterative reconstruction framework (PnP-MBIR) that integrates a deep convolutional denoiser trained in a 2-stage self-supervised Noise-to-Noise (N2N) scheme. The method alternates between enforcing sinogram-domain data fidelity and applying the learned image-domain denoiser within an optimization, enabling artifact suppression while maintaining anatomical structure. The 2-stage protocol enables fully self-supervised training from noisy data, followed by high-dose fine-tuning, ensuring the denoiser's robustness in the ultra-low-dose regime. Our method enables high-quality reconstructions at $\sim$70--80\% lower dose levels, while maintaining diagnostic fidelity comparable to standard full-dose scans. Quantitative evaluations using Gray-Level Co-occurrence Matrix (GLCM) features -- including contrast, homogeneity, entropy, and correlation -- confirm that the proposed method yields superior texture consistency and detail preservation over standalone deep learning and supervised PnP baselines. Qualitative and quantitative results on both simulated and clinical datasets demonstrate that our framework effectively reduces streaks and structured artifacts while preserving subtle tissue contrast, making it a promising tool for ULDCT reconstruction.