CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12304 > Article
Open Access Paper
17 October 2022 Sparsier2Sparse: weakly supervised learning for streak artifact reduction with unpaired sparse-view CT data
Seongjun Kim, Byeongjoon Kim, Jongduk Baek
Author Affiliations +
Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123040L (2022) https://doi.org/10.1117/12.2646428
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
Sparse-view computed tomography (CT) becomes a major concern in the medical imaging field due to its reduced X-ray radiation dose. Recently, various convolutional neural network (CNN)-based approaches have been proposed, requiring the pairs of full and sparse-view CT images for network training. However, these paired data acquisition is impractical or difficult in clinical practice. To handle this problem, we propose the weakly-supervised learning for streak artifact reduction with unpaired sparse-view CT data. For CNN training dataset, we generate the pairs of input and target images from the given sparse-view CT data. Then, we iteratively apply the trained network to given sparse-view CT images and acquire the prior images. As the success factor of our novel framework, we estimate the original streak artifacts in the given sparse-view CT images from the prior images and subtract the estimated streak artifacts from the given sparse-view CT images. As a result, the proposed method has the best performance of lesion detection compared to the other methods.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Seongjun Kim, Byeongjoon Kim, and Jongduk Baek "Sparsier2Sparse: weakly supervised learning for streak artifact reduction with unpaired sparse-view CT data", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123040L (17 October 2022); https://doi.org/10.1117/12.2646428
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
X-ray computed tomography
Computed tomography
X-rays
Medical imaging
X-ray imaging
Convolutional neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top