JSR-Net: A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data

Haimiao Zhang; Bin Dong; Baodong Liu

doi:10.1109/ICASSP.2019.8682178

JSR-Net: A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data

Haimiao Zhang, Bin Dong, Baodong Liu

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model based approach with deep architecture design of deep learning. A hybrid loss function is adopted to improve the performance of the JSR-Net making it more effective in protecting important image structures. Numerical experiments demonstrate that JSR-Net outperforms some latest model based reconstruction methods, as well as a recently proposed deep model.

Original language	English (US)
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3657-3661
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682178
State	Published - May 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: May 12 2019 → May 17 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country	United Kingdom
City	Brighton
Period	5/12/19 → 5/17/19

Keywords

Convolutional neural networks
Deep learning
Joint spatial-Radon domain reconstruction
Limited angle CT
Sparse-view CT

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2019.8682178

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Cite this

Zhang, H., Dong, B., & Liu, B. (2019). JSR-Net: A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 3657-3661). [8682178] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682178

JSR-Net : A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data. / Zhang, Haimiao; Dong, Bin; Liu, Baodong.

2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3657-3661 8682178 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, H, Dong, B & Liu, B 2019, JSR-Net: A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8682178, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 3657-3661, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 5/12/19. https://doi.org/10.1109/ICASSP.2019.8682178

Zhang H, Dong B, Liu B. JSR-Net: A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3657-3661. 8682178. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2019.8682178

Zhang, Haimiao ; Dong, Bin ; Liu, Baodong. / JSR-Net : A Deep Network for Joint Spatial-radon Domain CT Reconstruction from Incomplete Data. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3657-3661 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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