Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction

Understand

X-ray Computed Tomography (CT) imaging has been widely used in clinical diagnosis, non-destructive examination, and public safety inspection.

• Sparse-view (sparse view) CT has great potential in radiation dose reduction and scan acceleration.
• However, sparse view CT data is insufficient and traditional reconstruction results in severe streaking artifacts.
• In this work, based on deep learning, we compared image reconstruction performance for sparse view CT reconstruction with projection domain network, image domain network, and comprehensive network combining projection and image domains.