9 citations · 12 across the 11 of their papers we have counts for
5 papers · 1 filter
HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction
Hongli Chen, Pengcheng Fang, Yuxia Chen +6
Reconstructing high-fidelity MR images from undersampled k-space data remains a challenging problem in MRI. While Mamba variants for vision tasks offer promising long-range modelin…
SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Min Li, Chen Chen, Zhenghao Li +9
Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sourc…
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
Jin Liu, Qing Lin, Zhuang Xiong +7
Incoherent k-space undersampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will…
IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules
Min Li, Chen Chen, Zhuang Xiong +6
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potentia…
Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks
Yang Gao, Zhuang Xiong, Shanshan Shan +7
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) al…