17 citations · 28 across the 7 of their papers we have counts for
7 papers
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan +11
In the field of parallel imaging (PI), alongside image-domain regularization methods, substantial research has been dedicated to exploring -space interpolation. However, the int…
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…
Energetic Analysis on the Optimal Bounding Gaits of Quadrupedal Robots
Yasser G. Alqaham, Jing Cheng, Zhenyu Gan
It is often overlooked by roboticists when designing locomotion controllers for their legged machines, that energy consumption plays an important role in selecting the best gaits f…
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction
Zhuo-Xu Cui, Chentao Cao, Shaonan Liu +5
Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction. Most of these methods require a large amount of fully sampled MRI data as a traini…
K-UNN: k-Space Interpolation With Untrained Neural Network
Zhuo-Xu Cui, Sen Jia, Qingyong Zhu +7
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled tr…
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…