mlsanityData to 7 Oct 2026

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

arXiv 1703.00555 ↗First posted 1 Mar 2017cs.CVcited by 74

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Abstract (arXiv)

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23 ms, which is fast enough to enable real-time applications.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2017.

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Citing papers per year: 2021: 10; 2022: 16; 2023: 23; 2024: 14; 2025: 8; 2026: 3.

74 papers.

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  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  2. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  3. 2021Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problemcited by 53
  4. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 37
  5. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
  6. 2022Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imagingcited by 35
  7. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  8. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstructioncited by 20
  9. 2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 19
  10. 2024MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimationcited by 19
  11. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  12. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 13
38 more
  1. 2024Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRIcited by 13
  2. 2022Adversarial Robustness of MR Image Reconstruction under Realistic Perturbationscited by 11
  3. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 11
  4. 2023CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?cited by 11
  5. 2021MRI Reconstruction Using Deep Energy-Based Modelcited by 8
  6. 2024MambaRecon: MRI Reconstruction with Structured State Space Modelscited by 7
  7. 2024Physics-Driven Autoregressive State Space Models for Medical Image Reconstructioncited by 7
  8. 2021MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Predictioncited by 6
  9. 2022A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstructioncited by 6
  10. 2022Foreground-Background Separation through Concept Distillation from Generative Image Foundation Modelscited by 5
  11. 2023K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsetscited by 5
  12. 2024Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamicscited by 5
  13. 2021Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstructioncited by 4
  14. 2022K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstructioncited by 4
  15. 2022Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstructioncited by 4
  16. 2023CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstructioncited by 4
  17. 2023The Significance of Machine Learning in Clinical Disease Diagnosis: A Reviewcited by 4
  18. 2024Attention-aware non-rigid image registration for accelerated MR imagingcited by 4
  19. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 4
  20. 2023PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classificationcited by 3
  21. 2023Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Studycited by 3
  22. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
  23. 2023Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecturecited by 3
  24. 2024MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion modelcited by 3
  25. 2024A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learningcited by 3
  26. 2024MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learningcited by 3
  27. 2021A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priorscited by 2
  28. 2021Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural networkcited by 2
  29. 2021Contrastive Learning for Local and Global Learning MRI Reconstructioncited by 2
  30. 2022Reconstruction of Incomplete Wildfire Data using Deep Generative Modelscited by 2
  31. 2022Universal Generative Modeling for Calibration-free Parallel Mr Imagingcited by 2
  32. 2022Transformer Compressed Sensing via Global Image Tokenscited by 2
  33. 2023SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstructioncited by 2
  34. 2023Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworkscited by 2
  35. 2023Deep unrolling Shrinkage Network for Dynamic MR imagingcited by 2
  36. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2
  37. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  38. 2025Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstructioncited by 2

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