2 citations · 4 across the 3 of their papers we have counts for
3 papers
eess.IV2022★ 2 cited
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Zongjiang Tu, Die Liu, Xiaoqing Wang +6
Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still has some limitations, suc…
eess.IV2022
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Zongjiang Tu, Chen Jiang, Yu Guan +4
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to…
eess.IV2021★ 2 cited
MRI Reconstruction Using Deep Energy-Based Model
Yu Guan, Zongjiang Tu, Shanshan Wang +3
Purpose: Although recent deep energy-based generative models (EBMs) have shown encouraging results in many image generation tasks, how to take advantage of the self-adversarial cog…