Journal ArticleDOI
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
TLDR
A graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions and outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.About:
This article is published in Medical Image Analysis.The article was published on 2016-01-01. It has received 150 citations till now. The article focuses on the topics: Iterative reconstruction & Wavelet transform.read more
Citations
More filters
Journal ArticleDOI
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
Guang Yang,Simiao Yu,Hao Dong,Greg Slabaugh,Pier Luigi Dragotti,Xujiong Ye,Fangde Liu,Simon R. Arridge,Jennifer Keegan,Yike Guo,David N. Firmin +10 more
TL;DR: This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets.
Journal ArticleDOI
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
TL;DR: Two versions of a novel deep learning architecture are proposed, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements, which achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.
Journal ArticleDOI
Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
TL;DR: Wang et al. as discussed by the authors proposed a projected iterative soft thresholding algorithm (pISTA) and its acceleration pFISTA for CS-MRI image reconstruction, which exploit sparsity of the magnetic resonance (MR) images under the redundant representation of tight frames.
Journal ArticleDOI
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
TL;DR: Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.
Journal ArticleDOI
A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction
TL;DR: Wang et al. as discussed by the authors proposed the first deep learning model for multi-contrast CS-MRI reconstruction, which achieved information sharing through feature sharing units, which significantly reduced the number of model parameters.
References
More filters
Journal ArticleDOI
Causal dynamic MRI reconstruction via nuclear norm minimization
Angshul Majumdar,Rabab K. Ward +1 more
TL;DR: The proposed method reconstructs the difference between the images of previous and current time frames as a rank deficient matrix and is solved from the partially sampled k-space data via nuclear norm minimization to have similar reconstruction accuracy as the offline method and significantly higher accuracy compared to the online technique.
Journal ArticleDOI
Localized Spatio-Temporal Constraints for Accelerated CMR Perfusion
Mehmet Akcakaya,Tamer A. Basha,Silvio Pflugi,Murilo Foppa,Kraig V. Kissinger,Thomas H. Hauser,Reza Nezafat +6 more
TL;DR: To develop and evaluate an image reconstruction technique for cardiac MRI (CMR) perfusion that uses localized spatio‐temporal constraints.
Proceedings ArticleDOI
Improved compressed sensing MRI with multi-channel data using reweighted l 1 minimization
Ching-Hua Chang,Jim Ji +1 more
TL;DR: This paper proposes a method that extends the reweighted l1 minimization to the CS MRI with multi-channel data and shows that the new method can provide improved reconstruction quality.
Proceedings ArticleDOI
Combined compressed sensing and parallel mri compared for uniform and random cartesian undersampling of K-space
Daniel S. Weller,Jonathan R. Polimeni,Leo Grady,Lawrence L. Wald,Elfar Adalsteinsson,Vivek K Goyal +5 more
TL;DR: This paper investigates the choice of a suitable sampling pattern to accommodate both CS and parallel imaging, and a combined method named SpRING is described and extended to handle random undersampling.
Journal ArticleDOI
k-t sparse GROWL: Sequential combination of partially parallel imaging and compressed sensing in k-t space using flexible virtual coil
TL;DR: K‐t sparse Generalized GRAPPA fOr Wider readout Line can produce results with two times lower root‐mean‐square error than conventional channel‐by‐channel k‐t CS while consuming up to seven times less computational cost.