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JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA

Citation Author(s):
Haimiao Zhang, Bin Dong, Baodong Liu
Submitted by:
Haimiao Zhang
Last updated:
10 May 2019 - 3:25am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Haimiao Zhang
Paper Code:
1283

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 adapted 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.

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