5 citations · 15 across the 12 of their papers we have counts for
6 papers · 1 filter
Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
Wei Zhang, Zengwei Xiao, Hui Tao +3
Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in hig…
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
Bing Guan, Cailian Yang, Liu Zhang +5
The radiation dose in computed tomography (CT) examinations is harmful for patients but can be significantly reduced by intuitively decreasing the number of projection views. Reduc…
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
Hong Peng, Chen Jiang, Jing Cheng +4
Magnetic resonance imaging serves as an essential tool for clinical diagnosis. However, it suffers from a long acquisition time. The utilization of deep learning, especially the de…
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…
Universal Generative Modeling for Calibration-free Parallel Mr Imaging
Wanqing Zhu, Bing Guan, Shanshan Wang +2
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit…
Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning
Cailian Yang, Xianghao Liao, Yuhao Wang +2
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challengi…