1 citations · 1 across the 4 of their papers we have counts for
4 papers
Unsupervised Decomposition Networks for Bias Field Correction in MR Image
Dong Liang, Xingyu Qiu, Kuanquan Wang +3
Bias field, which is caused by imperfect MR devices or imaged objects, introduces intensity inhomogeneity into MR images and degrades the performance of MR image analysis methods.…
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI
Zhuo-Xu Cui, Congcong Liu, Chentao Cao +6
Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image…
Active CT Reconstruction with a Learned Sampling Policy
Ce Wang, Kun Shang, Haimiao Zhang +3
Computed tomography (CT) is a widely-used imaging technology that assists clinical decision-making with high-quality human body representations. To reduce the radiation dose posed…
Variable Augmented Network for Invertible MR Coil Compression
Xianghao Liao, Shanshan Wang, Lanlan Tu +3
A large number of coils are able to provide enhanced signal-to-noise ratio and improve imaging performance in parallel imaging. Nevertheless, the increasing growth of coil number s…