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
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Book ChapterDOI
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end task transformer network for joint MRI reconstruction and super-resolution, which allows representations and feature transmission to be shared between multiple tasks to achieve higher-quality, super-resolved and motion-artifacts-free images from highly undersampled and degenerated MRI data.
Proceedings Article
Compressed Sensing MRI Using a Recursive Dilated Network.
TL;DR: This work proposes a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters, and adopts dilated convolutions in each recursive block to aggregate multi-scale information within the MRI.
Book ChapterDOI
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network
TL;DR: SegNetMRI as mentioned in this paper uses an MRI reconstruction network with multiple cascaded blocks, each containing an encoder-decoder unit and a data fidelity unit, and a parallel MRI segmentation network having the same encoderdecoder structure.
Journal ArticleDOI
Co-Robust-ADMM-Net: Joint ADMM Framework and DNN for Robust Sparse Composite Regularization
Yunyi Li,Xiefeng Cheng,Guan Gui +2 more
TL;DR: This paper proposes a novel composite robust alternating direction method of multiplier network-based CS algorithm that can obtain higher reconstruction Peak signal-to-noise ratio than the existing state-of-the-art robust CS methods.
Journal ArticleDOI
Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI.
TL;DR: An image reconstruction approach named STDLR-SPiriT is proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT).
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.
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$rm K$ -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.