Clinical imaging protocols designed to accelerate acquisition or reduce radiation dose often lead to degraded image quality, compromising diagnostic confidence. The heterogeneity in degradation types and severities across imaging modalities further challenges the development of generalized restoration solutions. In this work, we introduce a unified framework that formulates medical image restoration as posterior sampling from self-supervised Latent Diffusion Models (LDMs), pretrained on multi-modal high-quality images. At the core of our method is an Estimation Posterior Sampling (EPS) strategy, which enhances both data fidelity and anatomical detail retention. EPS incorporates two key components: (i) estimated diffusion initialization to constrain sampling within the measurement-consistent solution space, and (ii) gradient-balanced optimization to adaptively trade off denoising strength and detail preservation throughout the diffusion trajectory. Unlike traditional task-specific models, our approach enables Plug-and-Play (PnP) deployment, supporting diverse degradations without retraining. Extensive experiments conducted on deterministic degradations (e.g., under-sampled MRI, sparse-view CT) and blind degradations (e.g., low-dose PET) across multiple degradation levels demonstrate superior quantitative and qualitative performance compared to both supervised baselines and state-of-the-art posterior sampling methods. Notably, our method achieves PSNR improvements of up to +2.9 dB (MRI), +1.1 dB (CT), and +0.9 dB (PET) in PnP mode. These results highlight the robustness and broad applicability of our framework for clinical deployment.

Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents. However, existing methods typically blur crucial details, leading to inaccurate lesion quantification. This paper proposes a lesion-perceived and quantification-consistent modulation (LeqMod) strategy for enhanced PET image denoising, via employing downstream lesion quantification analysis as auxiliary tools. The LeqMod is a plug-and-play design adaptable to a wide range of model architectures, modulating the sampling and optimization procedures of model training without adding any computational burden to the inference phase. Specifically, the LeqMod consists of two components, the lesion-perceived modulation (LeMod) and the multiscale quantification-consistent modulation (QuMod). The LeMod enhances lesion contrast and visibility by allocating higher sampling weights and stricter loss criteria to lesion-present samples determined by an auxiliary segmentation network than lesion-absent ones. The QuMod further emphasizes quantification accuracy for both the mean and maximum standardized uptake value ( ${\mathrm {SUV}}_{{\textit {mean}}}$ and ${\mathrm {SUV}}_{{\textit {max}}}$ ) across multiscale sub-regions throughout the entire image, thereby reducing biases of denoised results relative to high-count references. Experiments conducted on large PET datasets from multiple centers and vendors, and varying noise levels demonstrated the LeqMod efficacy across various denoising frameworks. Compared to frameworks without LeqMod, the integration of LeqMod reduces the lesion ${\mathrm {SUV}}_{{\textit {max}}}$ bias by 5.92% on average and increases the peak signal-to-noise ratio (PSNR) by 0.36 on average, when denoising images across participating sites. (Code is available at https://github.com/mhxiaaa/LeqMod_PET_denoising).