95 citations · 141 across the 32 of their papers we have counts for
8 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…
Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields
Taofeng Xie, Chentao Cao, Zhuoxu Cui +8
MRI and PET are important modalities and can provide complementary information for the diagnosis of brain diseases because MRI can provide structural information of brain and PET c…
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…
High-Frequency Space Diffusion Models for Accelerated MRI
Chentao Cao, Zhuo-Xu Cui, Yue Wang +5
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving t…
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…
PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
Chentao Cao, Zhuo-Xu Cui, Qingyong Zhu +3
Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. However, most of these methods represen…