55 citations · 83 across the 8 of their papers we have counts for
6 papers · 1 filter
Physics-Guided Diffusion Transformer with Spherical Harmonic Posterior Sampling for High-Fidelity Angular Super-Resolution in Diffusion MRI
Mu Nan, Taohui Xiao, Ruoyou Wu +4
Diffusion MRI (dMRI) angular super-resolution (ASR) aims to reconstruct high-angular-resolution (HAR) signals from limited low-angular-resolution (LAR) data without prolonging scan…
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
Shanshan Wang, Taohui Xiao, Qiegen Liu +1
Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed". Reducing the scanned measurem…
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
Yu Gong, Hongming Shan, Yueyang Teng +5
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However…
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset
Shanshan Wang, Yanxia Chen, Taohui Xiao +3
This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, t…
Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging
Yanxia Chen, Taohui Xiao, Cheng Li +2
Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated w…
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
Shanshan Wang, Huitao Cheng, Leslie Ying +5
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Differe…