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

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.

References in corpus (4)

• Deep Residual Learning for Image Recognition
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Cited by in corpus (4)

• A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction
• C-Arm Non-Circular Orbits: Geometric Calibration, Image Quality, and Avoidance of Metal Artifacts
• A Model-based Deep Learning Reconstruction for X-ray CT
• A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT