13 citations · 26 across the 6 of their papers we have counts for
6 papers
Deep unrolling Shrinkage Network for Dynamic MR imaging
Yinghao Zhang, Xiaodi Li, Weihang Li +1
Deep unrolling networks that utilize sparsity priors have achieved great success in dynamic magnetic resonance (MR) imaging. The convolutional neural network (CNN) is usually utili…
T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction
Yinghao Zhang, Peng Li, Yue Hu
The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction. Tensor low-rank methods preserve the inherent high-dimensional structure of data, allow…
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm Regularizations
Yinghao Zhang, Yue Hu
Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied…
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LTLR-Net)
Yinghao Zhang, Peng Li, Yue Hu
While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, tensor low-rank models have recently emerged as powerful…
A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery
Yue Hu, Xiaohan Liu, Mathews Jacob
Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized s…
Adaptive structured low rank algorithm for MR image recovery
Yue Hu, Xiaohan Liu, Mathews Jacob
We introduce an adaptive structured low rank algorithm to recover MR images from their undersampled Fourier coefficients. The image is modeled as a combination of a piecewise const…