139 citations · 232 across the 44 of their papers we have counts for
17 papers · 1 filter
Diffusion-Encoding Gaussian Field for Joint k-q dMRI Reconstruction
Zhibo Chen, Yajuan Huang, Yu Guan +3
Diffusion MRI requires repeated k-space acquisitions over multiple diffusion-encoding directions, making acquisition time dependent on both spatial and angular sampling. Existing j…
K-space Gaussian Representation for Parallel MRI
Yu Guan, Mingyu Hu, Jiale Hu +3
Accelerated magnetic resonance imaging (MRI) aims to recover the k-space signal from acquired measurements, where accurate estimation of missing samples is essential for high-fidel…
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
Yu Guan, Tianjia Huang, Qinrong Cai +3
Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete…
Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction
Zhibo Chen, Yu Guan, Yajuan Huang +5
Simultaneous multi-slice (SMS) imaging with in-plane undersampling enables highly accelerated MRI but yields a strongly coupled inverse problem with deterministic inter-slice inter…
K-Syn: K-space Data Synthesis in Ultra Low-data Regimes
Guan Yu, Zhang Jianhua, Liang Dong +1
Owing to the inherently dynamic and complex characteristics of cardiac magnetic resonance (CMR) imaging, high-quality and diverse k-space data are rarely available in practice, whi…
AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning
Wenxin Fan, Jian Cheng, Cheng Li +6
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstru…
A multi-modal vision-language model for generalizable annotation-free pathology localization
Hao Yang, Hong-Yu Zhou, Jiarun Liu +12
Existing deep learning models for defining pathology from clinical imaging data rely on expert annotations and lack generalization capabilities in open clinical environments. Here,…
Multimodal self-supervised learning for lesion localization
Hao Yang, Hong-Yu Zhou, Cheng Li +7
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capa…
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…
Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
Wei Zhang, Zengwei Xiao, Hui Tao +3
Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in hig…
Universal Generative Modeling for Calibration-free Parallel Mr Imaging
Wanqing Zhu, Bing Guan, Shanshan Wang +2
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit…
Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning
Cailian Yang, Xianghao Liao, Yuhao Wang +2
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challengi…
Variable Augmented Network for Invertible MR Coil Compression
Xianghao Liao, Shanshan Wang, Lanlan Tu +3
A large number of coils are able to provide enhanced signal-to-noise ratio and improve imaging performance in parallel imaging. Nevertheless, the increasing growth of coil number s…
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
Chen Hu, Cheng Li, Haifeng Wang +3
Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited…
Wavelet Transform-assisted Adaptive Generative Modeling for Colorization
Jin Li, Wanyun Li, Zichen Xu +2
Unsupervised deep learning has recently demonstrated the promise of producing high-quality samples. While it has tremendous potential to promote the image colorization task, the pe…
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Jing Cheng, Haifeng Wang, Yanjie Zhu +10
Medical imaging is playing a more and more important role in clinics. However, there are several issues in different imaging modalities such as slow imaging speed in MRI, radiation…
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
Ziwen Ke, Shanshan Wang, Huitao Cheng +4
Dynamic magnetic resonance (MR) imaging has generated great research interest, as it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging…