5 citations · 15 across the 12 of their papers we have counts for
12 papers
Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
Pengfei Yu, Bin Huang, Minghui Zhang +3
Score-based diffusion models have shown significant promise in the field of sparse-view CT reconstruction. However, the projection dataset is large and riddled with redundancy. Con…
Partition-based K-space Synthesis for Multi-contrast Parallel Imaging
Yuxia Huang, Zhonghui Wu, Xiaoling Xu +3
Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause…
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…
High-dimensional Assisted Generative Model for Color Image Restoration
Kai Hong, Chunhua Wu, Cailian Yang +4
This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks. Considering that t…
Joint Intensity-Gradient Guided Generative Modeling for Colorization
Kai Hong, Jin Li, Wanyun Li +4
This paper proposes an iterative generative model for solving the automatic colorization problem. Although previous researches have shown the capability to generate plausible color…
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
Yiling Liu, Qiegen Liu, Minghui Zhang +3
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (…
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
Siyuan Wang, Junjie Lv, Yuanyuan Hu +3
Compressive sensing is an impressive approach for fast MRI. It aims at reconstructing MR image using only a few under-sampled data in k-space, enhancing the efficiency of the data…