9 citations · 14 across the 8 of their papers we have counts for
10 papers
DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series
Tianyi Ding, Yang Gao, Zhuang Xiong +4
Background: Estimating subvoxel tissue fractions from magnetic resonance fingerprinting (MRF) is challenging. Conventional dictionary matching and segmentation pipelines can propag…
Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Tianyi Ding, Hongli Chen, Yang Gao +4
Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffer…
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…
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
Zhuang Xiong, Wei Jiang, Yang Gao +2
Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While super…
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…
Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping
Zhuang Xiong, Yang Gao, Feng Liu +1
Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep le…
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
Yang Gao, Zhuang Xiong, Amir Fazlollahi +8
Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the tradi…