mlsanityData to 7 Oct 2026

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

arXiv 1704.02422 ↗First posted 8 Apr 2017cs.CVcited by 231

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Cited by 22 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling. Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Secondly, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10s and, for the 2D case, each image frame can be reconstructed in 23ms, enabling real-time applications.

Key facts. Cited by 231 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 51; 2022: 59; 2023: 46; 2024: 28; 2025: 26; 2026: 21.

231 papers.

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  1. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  2. 2022ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removalcited by 102
  3. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  4. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model samplingcited by 59
  5. 2022Swin Transformer for Fast MRIcited by 56
  6. 2021Zero-Shot Self-Supervised Learning for MRI Reconstructioncited by 53
  7. 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
  8. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
  9. 2021Multi-Modal Transformer for Accelerated MR Imagingcited by 42
  10. 2022High-Frequency Space Diffusion Models for Accelerated MRIcited by 41
  11. 2021Memory-Augmented Deep Unfolding Network for Compressive Sensingcited by 38
  12. 2021AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysiscited by 38
38 more
  1. 2021Bayesian Uncertainty Estimation of Learned Variational MRI Reconstructioncited by 30
  2. 2022DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstructioncited by 30
  3. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  4. 2021Learning a Model-Driven Variational Network for Deformable Image Registrationcited by 25
  5. 2021Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimizationcited by 19
  6. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  7. 2021Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Datacited by 18
  8. 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Futurecited by 18
  9. 2021An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Datasetcited by 18
  10. 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
  11. 2021Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Networkcited by 16
  12. 2021Universal Undersampled MRI Reconstructioncited by 15
  13. 2021Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancementcited by 15
  14. 2023Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstructioncited by 15
  15. 2021Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRIcited by 14
  16. 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
  17. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 14
  18. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  19. 2021A review and experimental evaluation of deep learning methods for MRI reconstructioncited by 13
  20. 2021FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imagingcited by 13
  21. 2023Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstructioncited by 13
  22. 2023SADIR: Shape-Aware Diffusion Models for 3D Image Reconstructioncited by 12
  23. 2024Invertible Diffusion Models for Compressed Sensingcited by 12
  24. 2024Adaptive Compressed Sensing with Diffusion-Based Posterior Samplingcited by 12
  25. 2021Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstructioncited by 11
  26. 2022Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networkscited by 11
  27. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 11
  28. 2024Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstructioncited by 11
  29. 2021An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivitiescited by 10
  30. 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
  31. 2023Uncertainty Estimation and Propagation in Accelerated MRI Reconstructioncited by 10
  32. 2023Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Datacited by 10
  33. 202120-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstructioncited by 9
  34. 2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
  35. 2022Limitations of Deep Learning for Inverse Problems on Digital Hardwarecited by 9
  36. 2023PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstructioncited by 9
  37. 2024Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translationcited by 9
  38. 2024Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstructioncited by 9

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