Segmentation-Guided Accelerating Diffusion Model for Cardiac CT Motion Artifact Reduction via Limited-Angle Imaging

Coronary computed tomography angiography (CCTA) is a pivotal non-invasive imaging modality for diagnosing cardiac disease. However, due to the temporal resolution limitations, cardiac structures, specifically coronary arteries, may suffer from motion artifacts when CCTA is applied to patients with arrhythmias or high heart rates. Limited-angle CT (LA-CT) emerges as a promising alternative by significantly reducing the acquisition time, thereby mitigating the motion artifacts. Yet, LA-CT unavoidably leads to severe wedge artifacts, posing a significant challenge. Therefore, to reconstruct motion-free cardiac CT images while suppressing the wedge artifacts, we propose a Segmentation-Guided Accelerating Diffusion Model (SGADM) tailored for LA-CT imaging. While diffusion models have demonstrated exceptional performance in medical imaging, their extensive sampling procedures impose high computational costs, hindering clinical applicability. To address this issue, SGADM employs an innovative diffusion model that directly generates high-quality CT images. Moreover, SGADM adopts the diffusion perceptual loss to ensure data distribution consistency between two successive sampling steps. As a result, SGADM can provide satisfactory results in fewer than 10 steps. Additionally, SGADM incorporates segmentation guidance to enhance the spatial-positional accuracy of generated coronary arteries explicitly. Both quantitative and qualitative evaluations on simulated and real datasets reveal that the SGADM effectively restores high-quality CCTA images with minimal motion artifacts, highlighting its potential for clinical applications.