38 citations · 123 across the 27 of their papers we have counts for
6 papers · 1 filter
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Chentao Cao, Zhuo-Xu Cui, Jing Cheng +4
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the character…
Deep unfolding as iterative regularization for imaging inverse problems
Zhuo-Xu Cui, Qingyong Zhu, Jing Cheng +1
Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse proble…
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…
Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Yuanyuan Liu, Dong Liang, Zhuo-Xu Cui +7
Quantitative magnetic resonance (MR) T1\r{ho} mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinde…