activity
20172023
most citedLearning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

7 citations · 7 across the 9 of their papers we have counts for

collaborators

10 papers

eess.IV2023

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…

eess.IV2022

Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field

Qing Wu, Xin Li, Hongjiang Wei +2

Neural Radiance Field (NeRF) has widely received attention in Sparse-View Computed Tomography (SVCT) reconstruction tasks as a self-supervised deep learning framework. NeRF-based S…

eess.IV2022

A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation

Ruimin Feng, Qing Wu, Yuyao Zhang +1

Parallel imaging is a widely-used technique to accelerate magnetic resonance imaging (MRI). However, current methods still perform poorly in reconstructing artifact-free MRI images…

eess.IV2022

Continuous longitudinal fetus brain atlas construction via implicit neural representation

Lixuan Chen, Jiangjie Wu, Qing Wu +2

Longitudinal fetal brain atlas is a powerful tool for understanding and characterizing the complex process of fetus brain development. Existing fetus brain atlases are typically co…

eess.IV2022

Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image

Xuanyu Tian, Qing Wu, Hongjiang Wei +1

Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and characteristics of the observed sampl…

eess.IV2021

IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation

Qing Wu, Yuwei Li, Lan Xu +7

For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images an…