Accelerated Physics-Guided Diffusion Model for 3-D Limited-Angle Reconstruction of Cardiac Computed Tomography

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide. Computed tomography (CT) plays an important role in the diagnosis and management of CVDs. However, the temporal resolution of current cardiac CT imaging techniques is still insufficient to freeze the beating heart. One solution to improve temporal resolution is fast data acquisition for a limited-angle range. Unfortunately, this approach introduces significant limited-angle artifacts that compromise image quality. Diffusion models have shown promising results to address this issue by effectively reconstructing missing data, but the process is typically time-consuming. In this paper, we propose a novel method for 3D limited-angle reconstruction using a diffusion model, optimized to significantly reduce computational time. Specifically, our approach integrates the regularized derivative least square (RDLS) algorithm with denoising diffusion implicit models. The numerical simulations and clinical data experiments demonstrate that the proposed model outperforms the existing methods both quantitatively and qualitatively. Compared with DDS, our method delivers a 1.8× speedup on the simulated dataset and a 3.4× speedup on the real clinical dataset.