8 citations · 12 across the 6 of their papers we have counts for
6 papers
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Yi Zhang, Hu Chen, Wenjun Xia +5
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis a…
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Haimiao Zhang, Baodong Liu, Hengyong Yu +1
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model f…
JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data
Haimiao Zhang, Bin Dong, Baodong Liu
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 Re-weighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction
Haimiao Zhang, Bin Dong, Baodong Liu
High density implants such as metals often lead to serious artifacts in the reconstructed CT images which hampers the accuracy of image based diagnosis and treatment planning. In t…
PET image reconstruction with system matrix containing point spread function derived from single photon incidence response
Fan Xin, Wang Hai-Peng, Yun Ming-Kai +7
In positron emission tomography (PET) imaging, statistical iterative reconstruction (IR) techniques appear particularly promising since they can provide accurate system model. The…
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
Baodong Liu, Hengyong Yu, Scott S. Verbridge +2
Electron tomography usually suffers from so called missing wedge artifacts caused by limited tilt angle range. An equally sloped tomography (EST) acquisition scheme (which should b…