ArticleIEEE transactions on computational imaging2019
Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model.
Article in IEEE transactions on computational imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Truncated enhanced constraint for low-rank plus sparse in cardiac dynamic MRI reconstruction.Magnetic resonance letters · 2026Article
- Smooth optimization using global and local low-rank regularizers.SIAM journal on imaging sciences · 2026Article
- Spatiotemporal Maps for Dynamic MRI Reconstruction.IEEE transactions on computational imaging · 2026Article
- Dynamic Magnetic Resonance Imaging of Whole-Stomach Motility in Rats.NMR in biomedicine · 2025Article
- Cardiac MR image reconstruction using cascaded hybrid dual domain deep learning framework.PloS one · 2025Article
- SNAKE: A modular realistic fMRI data simulator from the space-time domain to k-space and back.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification.IEEE transactions on medical imaging · 2024Article
- Dynamic cardiac MRI with high spatiotemporal resolution using accelerated spiral-out and spiral-in/out bSSFP pulse sequences at 1.5 T.Magma (New York, N.Y.) · 2023Article
- Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR.Magnetic resonance in medicine · 2023Article
- Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation.IEEE transactions on medical imaging · 2022Article
- Magnetic Resonance Image under Variable Model Algorithm in Diagnosis of Patients with Spinal Metastatic Tumors.Contrast media & molecular imaging · 2021Article
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2 authors.
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Abstract
The low-rank plus sparse (L+S) decomposition model enables the reconstruction of under-sampled dynamic parallel magnetic resonance imaging (MRI) data. Solving for the low-rank and the sparse components involves non-smooth composite convex optimization, and algorithms for this problem can be categorized into proximal gradient methods and variable splitting methods. This paper investigates new efficient algorithms for both schemes. While current proximal gradient techniques for the L+S model involve the classical iterative soft thresholding algorithm (ISTA), this paper considers two accelerated alternatives, one based on the fast iterative shrinkage-thresholding algorithm (FISTA), and the other with the recent proximal optimized gradient method (POGM). In the augmented Lagrangian (AL) framework, we propose an efficient variable splitting scheme based on the form of the data acquisition operator, leading to simpler computation than the conjugate gradient (CG) approach required by existing AL methods. Numerical results suggest faster convergence of the efficient implementations for both frameworks, with POGM providing the fastest convergence overall and the practical benefit of being free of algorithm tuning parameters.
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