ArticleMagnetic resonance in medicine2020
Dynamic MR image reconstruction based on total generalized variation and low-rank decomposition.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Truncated enhanced constraint for low-rank plus sparse in cardiac dynamic MRI reconstruction.Magnetic resonance letters · 2026Article
- Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla.Frontiers in neuroscience · 2024Article
- A deep unrolled neural network for real-time MRI-guided brain intervention.Nature communications · 2023Article
- Compressed sensing on displacement signals measured with optical coherence tomography.Biomedical optics express · 2023Article
- A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T.Magma (New York, N.Y.) · 2023Article
- Simultaneous spatial and temporal regularization in low-dose dynamic contrast-enhanced CT cerebral perfusion studies.Journal of applied clinical medical physics · 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
- Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network.IEEE transactions on medical imaging · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
purposePropose a novel decomposition-based model employing the total generalized variation (TGV) and the nuclear norm, which can be used in compressed sensing-based dynamic MR reconstructions. THEORY AND
methodsWe employ the nuclear norm to represent the time-coherent background and the spatiotemporal TGV functional for the sparse dynamic component above. We first design an algorithm using the classical first-order primal-dual method for solving the proposed model and then give the norm estimation for the convergence condition. The proposed model is compared with the state-of-the-art methods on different data sets under different sampling schemes and acceleration factors.
resultsThe proposed model achieves higher SERs and SSIMs than kt-SLR, kt-RPCA, L+S, and ICTGV on cardiac perfusion and breast DCE-MRI data sets under both the pseudoradial and the Cartesian sampling schemes. In addition, the proposed model better suppresses the spatial artifacts and preserves the edges.
conclusionsThe proposed model outperforms the state-of-the-art methods and generates high-quality reconstructions under different sampling schemes and different acceleration factors.
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