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DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training

arXiv 1810.00302 ↗First posted 30 Sep 2018cs.CVcited by 26

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Abstract (arXiv)

Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multisupervised network training technique is developed to constrain the frequency domain information and reconstruction results at different levels. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2018.

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Citing papers per year: 2021: 5; 2022: 11; 2023: 6; 2024: 0; 2025: 4.

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  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  2. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  3. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  4. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  5. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  6. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  7. 2022Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonancecited by 21
  8. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstructioncited by 20
  9. 2021Deep Manifold Learning for Dynamic MR Imagingcited by 8
  10. 2021FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolutioncited by 7
  11. 2022SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imagingcited by 5
  12. 2025PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRIcited by 4
14 more
  1. 2022Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolutioncited by 3
  2. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
  3. 2023Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRIcited by 3
  4. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  5. 2023RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imagingcited by 2
  6. 2023Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problemscited by 2
  7. 2021SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imagingcited by 1
  8. 2025A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imagingcited by 1
  9. 2022A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstructioncited by 0
  10. 2022Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonancecited by 0
  11. 2022Universal Generative Modeling in Dual-domain for Dynamic MR Imagingcited by 0
  12. 2023k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstructioncited by 0
  13. 2025Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstructioncited by 0
  14. 2025Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstructioncited by 0