KEYWORDS: Image restoration, X-ray computed tomography, X-rays, X-ray imaging, X-ray sources, Image quality, Education and training, CT reconstruction, Data acquisition, Signal to noise ratio
A static, multi-source x-ray Computed Tomography (CT) system facilitates rapid multi-view x-ray radiography, significantly improving the efficiency of cargo scanning. However, reconstructing images from sparse-view x-ray data in cargo scanning is challenging, particularly when conventional deep learning reconstruction techniques are hampered by a scarcity of training data. This work proposes the application of Deep Image Prior (DIP), which does not require training data, to reduce undersampling reconstruction artifacts arising from sparse-view and restricted opening angle acquisition in x-ray CT systems tailored for large-scale cargo scanning in harbors. The work particularly targets a rectangular multi-source x-ray CT system, featuring up to 40 equidistantly distributed static x-ray sources with a 30-degree opening angle. Our study demonstrates that DIP improves the quality of of sparse-view cargo CT in terms of PSNR and SSIM compared to traditional reconstruction 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.