ArticleIEEE signal processing magazine2020
Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning.
Article in IEEE signal processing magazine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- Smooth optimization using global and local low-rank regularizers.SIAM journal on imaging sciences · 2026Article
- Rapid quantitative MRI at 46 mT: Accelerated TMagnetic resonance in medicine · 2025Article
- Dynamic field mapping and distortion correction using single-shot blip-rewound EPI and joint multi-echo reconstruction.Magnetic resonance in medicine · 2024Article
- High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models.IEEE transactions on bio-medical engineering · 2024Article
- Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification.Magnetic resonance in medicine · 2024Article
- New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI.IEEE transactions on medical imaging · 2024Article
- Locally low-rank denoising in transform domains.bioRxiv : the preprint server for biology · 2023Article
- Complexities of deep learning-based undersampled MR image reconstruction.European radiology experimental · 2023Review
- Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T.NeuroImage · 2023Article
- Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping.Magnetic resonance in medicine · 2023Article
- B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI.IEEE transactions on medical imaging · 2022Article
- Reducing the Effects of Motion Artifacts in fMRI: A Structured Matrix Completion Approach.IEEE transactions on medical imaging · 2022Article
- Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction.IEEE transactions on medical imaging · 2021Article
- Fast data-driven learning of parallel MRI sampling patterns for large scale problems.Scientific reports · 2021Article
- High-dimensional fast convolutional framework (HICU) for calibrationless MRI.Magnetic resonance in medicine · 2021Article
- RECONSTRUCTION AND SEGMENTATION OF PARALLEL MR DATA USING IMAGE DOMAIN DEEP-SLR.Proceedings. IEEE International Symposium on Biomedical Imaging · 2021Article
- Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods.IEEE transactions on computational imaging · 2021Article
- Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR).IEEE transactions on medical imaging · 2020Article
- J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction.IEEE journal of selected topics in signal processing · 2020Article
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3 authors.
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Abstract
In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation. This framework is centered on the fundamental duality between the compactness (e.g., sparsity) of the continuous signal and the rank of a structured matrix, whose entries are functions of the signal. This property enables the reformulation of the signal recovery as a low-rank structured matrix completion, which comes with performance guarantees. We will also review fast algorithms that are comparable in complexity to current compressed sensing methods, which enables the application of the framework to large-scale magnetic resonance (MR) recovery problems. The remarkable flexibility of the formulation can be used to exploit signal properties that are difficult to capture by current sparse and low-rank optimization strategies. We demonstrate the utility of the framework in a wide range of MR imaging (MRI) applications, including highly accelerated imaging, calibration-free acquisition, MR artifact correction, and ungated dynamic MRI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.