MISD: Model-Informed Stable Diffusion Model for Limited Noisy Data CT Reconstruction

Limited data computed tomography (LDCT) plays a critical role in accelerating the scanning process and reducing radiation exposure for patients. However, LDCT reconstruction is inherently an ill-posed inverse problem, often resulting in pronounced edge artifacts and the loss of fine structural details. In recent years, score-based generative models (SGMs) have shown great promise in LDCT reconstruction by alleviating the ill-posedness and enabling high-fidelity image recovery in the case of noise-free condition. However, in practical CT systems, measurement data is often contaminated by noise. The coexistence of noise and limited data presents significant challenges for SGM-based image reconstruction methods. To address this challenge, this study proposes a Model-Informed Stable Diffusion (MISD) model which integrates a sampling process with a generative prior in the image-space module and a physics prior in the projection-space module. The projection-space module incorporates physical information to establish a noise suppression mechanism, effectively reducing the impact of noise. At the same time, the image-space module uses a generative model to progressively reconstruct clear structures and features from an initial state characterized by pure noise. Together, these two modules form a cohesive mathematical framework, utilizing iterative optimization to gradually minimize the effects of noise and artifacts. Experimental results show that the MISD method consistently achieves higher quantitative metrics and recovers finer structural details than other state-of-the-art reconstruction techniques, both on simulated and real datasets.