17 citations · 28 across the 15 of their papers we have counts for
15 papers
CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction
Qingyong Zhu, Yumin Tan, Xiang Gu +1
Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction. Representative FU-DG…
Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction
Baoqing Li, Yuanyuan Liu, Congcong Liu +6
Dynamic magnetic resonance imaging (dMRI) captures temporally-resolved anatomy but is often challenged by limited sampling and motion-induced artifacts. Conventional motion-compens…
Self-supervised Deep Unrolled Model with Implicit Neural Representation Regularization for Accelerating MRI Reconstruction
Jingran Xu, Yuanyuan Liu, Yuanbiao Yang +7
Magnetic resonance imaging (MRI) is a vital clinical diagnostic tool, yet its application is limited by prolonged scan times. Accelerating MRI reconstruction addresses this issue b…
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
Yuliang Zhu, Jing Cheng, Qi Xie +7
Dynamic Magnetic Resonance Imaging (MRI) exhibits transformation symmetries, including spatial rotation symmetry within individual frames and temporal symmetry along the time dimen…
Unsupervised patch-based dynamic MRI reconstruction using learnable tensor function with implicit neural representation
Yuanyuan Liu, Yuanbiao Yang, Jing Cheng +8
Dynamic MRI suffers from limited spatiotemporal resolution due to long acquisition times. Undersampling k-space accelerates imaging but makes accurate reconstruction challenging. S…
MR Optimized Reconstruction of Simultaneous Multi-Slice Imaging Using Diffusion Model
Ting Zhao, Zhuoxu Cui, Sen Jia +6
Diffusion model has been successfully applied to MRI reconstruction, including single and multi-coil acquisition of MRI data. Simultaneous multi-slice imaging (SMS), as a method fo…
LINEAR: Learning Implicit Neural Representation With Explicit Physical Priors for Accelerated Quantitative T1rho Mapping
Yuanyuan Liu, Jinwen Xie, Zhuo-Xu Cui +4
Quantitative T1rho mapping has shown promise in clinical and research studies. However, it suffers from long scan times. Deep learning-based techniques have been successfully appli…
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…
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…
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…
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI
Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu +8
Recently, model-driven deep learning unrolls a certain iterative algorithm of a regularization model into a cascade network by replacing the first-order information (i.e., (sub)gra…