3 citations · 5 across the 13 of their papers we have counts for
13 papers
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI
Ruoyou Wu, Jian Cheng, Cheng Li +5
Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affect…
AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-preserving Model-based Deep Learning
Wenxin Fan, Jian Cheng, Cheng Li +4
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and eddy current, leading to…
DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging
Wenxin Fan, Jian Cheng, Qiyuan Tian +4
Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep un…
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning
Ruoyou Wu, Jian Cheng, Cheng Li +5
Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, th…
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…
Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging
Jing Yang, Jian Cheng, Cheng Li +4
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in…
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search
Ruoyou Wu, Cheng Li, Juan Zou +1
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstructio…
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction
Ruoyou Wu, Cheng Li, Juan Zou +1
Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions. Federated learning,…
Self-Supervised Federated Learning for Fast MR Imaging
Juan Zou, Cheng Li, Ruoyou Wu +3
Federated learning (FL) based magnetic resonance (MR) image reconstruction can facilitate learning valuable priors from multi-site institutions without violating patient's privacy…
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data
Ruoyou Wu, Cheng Li, Juan Zou +3
Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robu…
Iterative Data Refinement for Self-Supervised MR Image Reconstruction
Xue Liu, Juan Zou, Xiawu Zheng +3
Magnetic Resonance Imaging (MRI) has become an important technique in the clinic for the visualization, detection, and diagnosis of various diseases. However, one bottleneck limita…
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Juan Zou, Cheng Li, Sen Jia +4
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampl…
PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging
Shanshan Wang, Ruoyou Wu, Cheng Li +5
With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in…