95 citations · 194 across the 36 of their papers we have counts for
38 papers
SGAM: Shared Gaussian Geometry with Implicit Amplitude Modeling for Scan-Specific 3D Multi-Contrast MRI Reconstruction
Jingran Xu, Dong Liang, Yuanyuan Liu +2
Three-dimensional (3D) multi-contrast magnetic resonance imaging (MCMRI) provides rich anatomical and quantitative information but requires long acquisition times, motivating k-spa…
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…
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…
Accurate myocardial T1 mapping at 5T using an improved MOLLI method: A validation study
Linqi Ge, Yinuo Zhao, Yubo Guo +7
Background: Accurate myocardial T1 mapping at 5T remains a technical challenge due to field inhomogeneity and prolonged T1 values. The aim of this study is to develop an accurate a…
Myocardial T1 mapping at 5T using multi-inversion recovery real-time spoiled GRE
Linqi Ge, Yihang Zhang, Huibin Zhu +6
Objective: To develop an accurate myocardial T1 mapping technique at 5T using Look-Locker-based multiple inversion-recovery with the real-time spoiled gradient echo (GRE) acquisiti…
Guided MRI Reconstruction via Schrödinger Bridge
Yue Wang, Yuanbiao Yang, Zhuo-xu Cui +5
Magnetic Resonance Imaging (MRI) is an inherently multi-contrast modality, where cross-contrast priors can be exploited to improve image reconstruction from undersampled data. Rece…
RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping
Chiyi Huang, Longwei Sun, Dong Liang +3
Cardiac T1 mapping can evaluate various clinical symptoms of myocardial tissue. However, there is currently a lack of effective, robust, and efficient methods for motion correction…
Quantum Neural Network for Accelerated Magnetic Resonance Imaging
Shuo Zhou, Yihang Zhou, Congcong Liu +4
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms t…
Optimized Magnetic Resonance Fingerprinting Using Ziv-Zakai Bound
Chaoguang Gong, Yue Hu, Peng Li +6
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative imaging technique within the field of Magnetic Resonance Imaging (MRI), offers comprehensive insight…
Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
Ling Lin, Yihang Zhou, Zhanqi Hu +8
Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification mo…
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…
Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model
Lang Zhang, Jinling He, Dong Liang +2
Magnetic resonance diffusion tensor imaging (DTI) is a critical tool for neural disease diagnosis. However, long scan time greatly hinders the widespread clinical use of DTI. To ac…
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection
Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng +7
Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the sta…
Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction
Taofeng Xie, Zhuo-Xu Cui, Chen Luo +10
Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, t…
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging
Yuanyuan Liu, Zhuo-Xu Cui, Shucong Qin +6
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3…
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…
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis
Fanshi Li, Zhihui Wang, Yifan Guo +6
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance. The following are the paper's main contribut…
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…
Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model
Taofeng Xie, Chentao Cao, Zhuoxu Cui +14
MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves…
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
Zhuo-Xu Cui, Chentao Cao, Yue Wang +6
Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, exi…
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…
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…