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
A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data
TL;DR: A novel variational model is proposed that enforces the sparsity of the underlying image in terms of its spatial finite differences and representation with respect to a dictionary to improve the robustness to parameter selection.
Journal ArticleDOI
Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization.
TL;DR: Numerical simulation results on vivo magnetic resonance images demonstrate that the proposed PBDW-based MRI reconstruction is improved from two aspects: an efficient non-convex minimization algorithm is modified to enhance image quality and P BDW are extended into shift-invariant discrete wavelet domain to enhance the ability of transform on sparsifying piecewise smooth image features.
Journal ArticleDOI
Sparse representation-based MRI super-resolution reconstruction
TL;DR: A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries.
Journal ArticleDOI
Activelets: Wavelets for sparse representation of hemodynamic responses
Ildar Khalidov,Jalal M. Fadili,François Lazeyras,Dimitri Van De Ville,Dimitri Van De Ville,Michael Unser +5 more
TL;DR: New wavelet bases are introduced, termed ''activelets'', which sparsify the activity- related BOLD signal and the importance of the activelet basis and the non-linear sparse recovery algorithm is demonstrated by comparison against classical B-spline wavelets and linear regularization, respectively.
Journal ArticleDOI
Efficient k-space sampling by density-weighted phase-encoding
TL;DR: Density‐weighted phase‐encoding combines the improved shape of the spatial response function and the high SNR of acquisition‐weighting with an extended field of view to improve the localization of MRI experiments.