32 citations · 95 across the 33 of their papers we have counts for
33 papers
MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessment
Fankai Jia, Daisong Gan, Zhe Zhang +4
Magnetic resonance imaging (MRI) quality assessment is crucial for clinical decision-making, yet remains challenging due to data scarcity and protocol variability. Traditional appr…
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…
K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging
Ting Zhao, Zhuoxu Cui, Congcong Liu +4
Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning tim…
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…
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…
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…
Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model
Taofeng Xie, Zhuoxu Cui, Congcong Liu +9
PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming by PET-MRI systems. We aim to accelerate MRI and improve PET…
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…
Analysis of osteoporotic tissue using combination nonlinear optical imaging
Bryan Semon, Michael Jaffe, Haifeng Wang +2
Currently, a large number of stored tissue samples are unavailable for spectroscopic study without the time consuming and destructive process of paraffin removal. Instead, a struct…
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…
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…
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…
Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging
Hui Tao, Haifeng Wang, Shanshan Wang +3
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise a…
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…
Discriminating Quantum States with Quantum Machine Learning
David Quiroga, Prasanna Date, Raphael C. Pooser
Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algeb…