mlsanityData to 7 Oct 2026

Deep Low-rank plus Sparse Network for Dynamic MR Imaging

arXiv 2010.13677 ↗First posted 26 Oct 2020eess.IV cs.CV cs.LGcited by 32

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Abstract (arXiv)

In dynamic magnetic resonance (MR) imaging, low-rank plus sparse (L+S) decomposition, or robust principal component analysis (PCA), has achieved stunning performance. However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods. Many deep learning approaches have been proposed to address these issues, but few of them use a low-rank prior. In this paper, a model-based low-rank plus sparse network, dubbed L+S-Net, is proposed for dynamic MR reconstruction. In particular, we use an alternating linearized minimization method to solve the optimization problem with low-rank and sparse regularization. Learned soft singular value thresholding is introduced to ensure the clear separation of the L component and S component. Then, the iterative steps are unrolled into a network in which the regularization parameters are learnable. We prove that the proposed L+S-Net achieves global convergence under two standard assumptions. Experiments on retrospective and prospective cardiac cine datasets show that the proposed model outperforms state-of-the-art CS and existing deep learning methods and has great potential for extremely high acceleration factors (up to 24x).

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 10; 2023: 6; 2024: 6; 2025: 7; 2026: 1.

32 papers.

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  1. 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 145
  2. 2023RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detectioncited by 43
  3. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  4. 2022Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imagingcited by 35
  5. 2022Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstructioncited by 23
  6. 2023Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstructioncited by 15
  7. 2022Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imagingcited by 11
  8. 2025RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentationcited by 11
  9. 2023Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimationcited by 8
  10. 2023Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modelingcited by 8
  11. 2021Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRIcited by 5
  12. 2022SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imagingcited by 5
20 more
  1. 2022Deep unfolding as iterative regularization for imaging inverse problemscited by 5
  2. 2022T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstructioncited by 4
  3. 2024Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRIcited by 4
  4. 2024DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Modelscited by 4
  5. 2025PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRIcited by 4
  6. 2023Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRIcited by 3
  7. 2024SRE-CNN: A Spatiotemporal Rotation-Equivariant CNN for Cardiac Cine MR Imagingcited by 3
  8. 2023Deep unrolling Shrinkage Network for Dynamic MR imagingcited by 2
  9. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  10. 2022K-UNN: k-Space Interpolation With Untrained Neural Networkcited by 1
  11. 2024Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularizationcited by 1
  12. 2024Attention Incorporated Network for Sharing Low-rank, Image and K-space Information during MR Image Reconstruction to Achieve Single Breath-hold Cardiac Cine Imagingcited by 1
  13. 2025JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRIcited by 1
  14. 2022A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstructioncited by 0
  15. 2022PS-Net: Learned Partially Separable Model for Dynamic MR Imagingcited by 0
  16. 2025Super-temporal-resolution Photoacoustic Imaging with Dynamic Reconstruction through Implicit Neural Representation in Sparse-viewcited by 0
  17. 2025DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstructioncited by 0
  18. 2025Compressive Imaging Reconstruction via Tensor Decomposed Multi-Resolution Grid Encodingcited by 0
  19. 2025Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstructioncited by 0
  20. 2026DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstructioncited by 0