104 citations · 217 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography
Qing Wu, Ruimin Feng, Hongjiang Wei +2
In the present work, we propose a Self-supervised COordinate Projection nEtwork (SCOPE) to reconstruct the artifacts-free CT image from a single SV sinogram by solving the inverse…
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
Qing Wu, Yuwei Li, Yawen Sun +4
High Resolution (HR) medical images provide rich anatomical structure details to facilitate early and accurate diagnosis. In MRI, restricted by hardware capacity, scan time, and pa…
MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping
Ruimin Feng, Jiayi Zhao, He Wang +8
Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse…
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Hongjiang Wei, Steven Cao, Yuyao Zhang +4
Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing step…
Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease
Alexandra Badea, Natalie A Delpratt, RJ Anderson +7
To understand multifactorial conditions such as Alzheimers disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help unc…