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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.
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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.

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Citations
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Journal ArticleDOI

MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform

TL;DR: A new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI, which recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.
Journal ArticleDOI

Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI

TL;DR: In this paper , an isotropic MC image regularizer was proposed for image reconstruction in fast protocols of MC parallel MRI, potentially reducing patient discomfort in otherwise long and time-consuming scans.
Posted Content

DONet: Dual-Octave Network for Fast MR Image Reconstruction

TL;DR: In this article, the authors proposed the Dual-Octave Network (DONet), which is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data, for fast parallel MR image reconstruction.
Book ChapterDOI

Deep Cascade Wavelet Network for Compressed Sensing-MRI

TL;DR: A deep cascade wavelet network (DCWN) is designed to solve the CS-MRI problem, which may greatly reduce the training burden for reconstructs high-frequency information, and provide more structural information.
Proceedings ArticleDOI

Research on A Multi-slice MRI Reconstruction Algorithm Based on Deep Learning

TL;DR: In this paper , the DISN model engaged in network training through deep learning and carried out multi-slice reconstruction under different conditions through controlling the variables, and the experimental results showed that the effect of multiscale network reconstruction was always better than that of single-slice network reconstruction.
References
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Journal ArticleDOI

Image quality assessment: from error visibility to structural similarity

TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Journal ArticleDOI

$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Journal ArticleDOI

An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint

TL;DR: It is proved that replacing the usual quadratic regularizing penalties by weighted 𝓁p‐penalized penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem.
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