38 citations · 119 across the 15 of their papers we have counts for
15 papers
DSA-SRGS: Super-Resolution Gaussian Splatting for Dynamic Sparse-View DSA Reconstruction
Shiyu Zhang, Zhicong Wu, Huangxuan Zhao +7
Digital subtraction angiography (DSA) is a key imaging technique for the auxiliary diagnosis and treatment of cerebrovascular diseases. Recent advancements in gaussian splatting an…
Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model
Yu Guan, Kunlong Zhang, Qi Qi +5
Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great poten…
Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
Chuanming Yu, Yu Guan, Ziwen Ke +2
Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the rec…
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI
Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu +8
Recently, model-driven deep learning unrolls a certain iterative algorithm of a regularization model into a cascade network by replacing the first-order information (i.e., (sub)gra…
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
Wenqi Huang, Sen Jia, Ziwen Ke +4
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution…
Deep Manifold Learning for Dynamic MR Imaging
Ziwen Ke, Zhuo-Xu Cui, Wenqi Huang +8
Purpose: To develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled me…
Deep Low-rank plus Sparse Network for Dynamic MR Imaging
Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui +6
In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the…
Deep Low-rank Prior in Dynamic MR Imaging
Ziwen Ke, Wenqi Huang, Jing Cheng +8
The deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, all of these methods are only driven by the sparse prior of MR images, while the…
An Unsupervised Deep Learning Method for Multi-coil Cine MRI
Ziwen Ke, Jing Cheng, Leslie Ying +3
Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersamp…
A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems
Huangxuan Zhao, Ziwen Ke, Ningbo Chen +8
Deconvolution is the most commonly used image processing method to remove the blur caused by the point-spread-function (PSF) in optical imaging systems. While this method has been…
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset
Shanshan Wang, Yanxia Chen, Taohui Xiao +3
This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, t…
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
Shanshan Wang, Huitao Cheng, Leslie Ying +5
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Differe…
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks
Dong Liang, Jing Cheng, Ziwen Ke +1
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields a…
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
Ziwen Ke, Shanshan Wang, Huitao Cheng +4
Dynamic magnetic resonance (MR) imaging has generated great research interest, as it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging…
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
Shanshan Wang, Ziwen Ke, Huitao Cheng +4
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction pro…