104 citations · 232 across the 22 of their papers we have counts for
7 papers · 1 filter
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Ruimin Feng, Qing Wu, Jie Feng +4
Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an i…
JSMoCo: Joint Coil Sensitivity and Motion Correction in Parallel MRI with a Self-Calibrating Score-Based Diffusion Model
Lixuan Chen, Xuanyu Tian, Jiangjie Wu +4
Magnetic Resonance Imaging (MRI) stands as a powerful modality in clinical diagnosis. However, it is known that MRI faces challenges such as long acquisition time and vulnerability…
Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
Qi Wu, Yuyao Zhang, Marawan Elbatel
Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability. One such model, the Segment Anythi…
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
Qing Wu, Lixuan Chen, Ce Wang +4
Emerging neural reconstruction techniques based on tomography (e.g., NeRF, NeAT, and NeRP) have started showing unique capabilities in medical imaging. In this work, we present a n…
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis
Xiaozhao Liu, Mianxin Liu, Lang Mei +4
To characterize atypical brain dynamics under diseases, prevalent studies investigate functional magnetic resonance imaging (fMRI). However, most of the existing analyses compress…
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI
Haonan Zhang, Yuhan Zhang, Qing Wu +7
In this paper, we propose an efficient self-supervised arbitrary-scale super-resolution (SR) framework to reconstruct isotropic magnetic resonance (MR) images from anisotropic MRI…
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
Jie Feng, Ruimin Feng, Qing Wu +3
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. H…