9 citations · 12 across the 11 of their papers we have counts for
11 papers
SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction
Pengcheng Fang, Hongli Chen, Fangfang Tang +3
Accelerated MRI reconstruction requires recovering missing details while preserving anatomically coherent structures across large spatial regions. State-space models such as Mamba…
Design and Construction of a Dedicated Radiolucent 8-element Flexible Radiofrequency (RF) Torso Coil for the 1.0T Australian MRI-Linac System
Mingyan Li, Ewald Weber, David E. J. Waddington +6
Magnetic resonance imaging-guided linear accelerators (MRI-Linacs) are an emerging treatment technology that enable online soft-tissue visualisation and adaptive radiotherapy. The…
HiFi-MambaV2: Hierarchical Shared-Routed MoE for High-Fidelity MRI Reconstruction
Pengcheng Fang, Hongli Chen, Guangzhen Yao +5
Reconstructing high-fidelity MR images from undersampled k-space data requires recovering high-frequency details while maintaining anatomical coherence. We present HiFi-MambaV2, a…
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…
Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers
Efrat Shimron, Shanshan Shan, James Grover +7
Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal…
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…
Image Reconstruction with B0 Inhomogeneity using an Interpretable Deep Unrolled Network on an Open-bore MRI-Linac
Shanshan Shan, Yang Gao, David E. J. Waddington +8
MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distorti…
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…
Distortion-Corrected Image Reconstruction with Deep Learning on an MRI-Linac
Shanshan Shan, Yang Gao, Paul Z. Y. Liu +5
Magnetic resonance imaging (MRI) is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometr…