Publication
Title
The deep steerable convolutional framelet network for suppressing directional artifacts in X-ray tomosynthesis
Author
Abstract
Tomographic artifacts are unwanted distortions and/or structures not present in the scanned body that may appear in the reconstructed images. Recent deep learning-based methods for suppressing artifacts in tomographic images are currently not informed by the nature of these artifacts in the design of their network architectures. In this work, we present the Deep Steerable Convolutional Framelet Network (DSCFN), inspired by the theory of deep convolutional framelets, which exploits the regular pattern of ripple artifacts presented in sparse-view X-ray tomosynthesis images. Experiments with simulated data show that the DSCFN outperforms the regular U-net and its deep convolutional framelet, the tight U-net, in terms of PSNR.
Language
English
Source (journal)
Signal Processing Conference (EUSIPCO), European
Source (book)
2023 31st European Signal Processing Conference (EUSIPCO), 04-08 September 2023, Helsinki, Finland
Publication
IEEE , 2023
ISSN
2076-1465
ISBN
978-94-6459-360-0
DOI
10.23919/EUSIPCO58844.2023.10289781
Volume/pages
p. 880-884
Full text (Publisher's DOI)
Full text (open access)
The author-created version that incorporates referee comments and is the accepted for publication version Available from 01.05.2024
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
Next generation X-ray metrology for meeting industry standards (MetroFlex).
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 14.12.2023
Last edited 15.12.2023
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