50 citations · 62 across the 5 of their papers we have counts for
5 papers
Fast Controllable Diffusion Models for Undersampled MRI Reconstruction
Wei Jiang, Zhuang Xiong, Feng Liu +2
Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizabi…
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…
Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)
Zhuang Xiong, Yang Gao, Yin Liu +4
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan paramet…
Universal Undersampled MRI Reconstruction
Xinwen Liu, Jing Wang, Feng Liu +1
Deep neural networks have been extensively studied for undersampled MRI reconstruction. While achieving state-of-the-art performance, they are trained and deployed specifically for…
xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks
Yang Gao, Xuanyu Zhu, Bradford A. Moffat +6
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image re…