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

TL;DR

This paper compares deep learning approaches in projection, image, and combined domains for sparse-view X-ray CT reconstruction, demonstrating that combined networks yield the best results in artifact reduction and detail preservation.

Contribution

It introduces a comprehensive deep learning network that integrates projection and image domain information for improved sparse-view CT reconstruction.

Findings

Deep learning effectively reduces streaking artifacts.

Combined projection and image domain network achieves superior reconstruction quality.

Networks preserve high-frequency structural details.

Abstract

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. Our study is executed with numerical simulated projection of CT images from real scans. Results demonstrated deep learning networks can effectively reconstruct rich high frequency structural information without streaking artefact commonly seen in sparse view CT. A comprehensive network combining deep learning in both projection domain and image domain can get best results.