activity
20182022
most citedBrain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

224 citations · 415 across the 8 of their papers we have counts for

collaborators

8 papers

eess.IV2022★ 20 cited

Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction

Zihao Chen, Yuhua Chen, Yibin Xie +2

Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-…

cs.CV2020★ 31 cited

MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better

Yuhua Chen, Anthony G. Christodoulou, Zhengwei Zhou +3

High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical information that is critical for diagnosis in the clinical application. However, HR MRI typically…

eess.IV2019★ 91 cited

Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks

Yuhua Chen, Dan Ruan, Jiayu Xiao +6

Segmentation of multiple organs-at-risk (OARs) is essential for radiation therapy treatment planning and other clinical applications. We developed an Automated deep Learning-based…

eess.IV2019★ 25 cited

Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking

Yuhua Chen, Jaime L. Shaw, Yibin Xie +2

High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical a…

eess.IV2019

Enhanced generative adversarial network for 3D brain MRI super-resolution

Jiancong Wang, Yuhua Chen, Yifan Wu +2

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging bu…

cs.CV2018★ 24 cited

Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network

Yuhua Chen, Feng Shi, Anthony G. Christodoulou +3

High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI con…

cs.CV2018

Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network

Siming Yan, Feng Shi, Yuhua Chen +5

Coronary calcium causes beam hardening and blooming artifacts on cardiac computed tomography angiography (CTA) images, which lead to overestimation of lumen stenosis and reduction…

cs.CV2018★ 224 cited

Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

Yuhua Chen, Yibin Xie, Zhengwei Zhou +3

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial…