上海科技大学知识管理系统

Cardiac computed tomography (CT) imaging faces challenges from cardiac and respiratory motion, which can result in motion artifacts. We have previously developed an innovative Triple-Source CT (TSCT) architecture which enables parallel acquisition of three projections to improve temporal resolution. However, forward and cross-scattering induced by multi-source exposures can severely degrade image quality. In this work, we evaluate various scatter correction approaches including a data-driven deep learning approach to mitigate scatter in a physical TSCT system. Phantom studies were performed under various configurations to investigate scatter effects and evaluate image quality pre- and post-correction. Evaluation metrics including HU profiles, HU uniformity and contrast-to-noise ratio (CNR) were analyzed. Among all evaluated scatter mitigation methods, the collimator-based hardware method achieved the best performance. Among all evaluated software-based methods, our deep-learning method performed slightly better than other deep learning methods. © 2024 SPIE.