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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.