mlsanityData to 7 Oct 2026

Deep learning for undersampled MRI reconstruction

arXiv 1709.02576 ↗First posted 8 Sep 2017stat.ML cs.LG physics.med-phcited by 91

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Abstract (arXiv)

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, very few low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of Fourier transforms of the subsampled and fully sampled k-space data. Numerous experiments show the remarkable performance of the proposed method; only 29% of k-space data can generate images of high quality as effectively as standard MRI reconstruction with fully sampled data.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2017.

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Citing papers per year: 2021: 17; 2022: 24; 2023: 21; 2024: 13; 2025: 12; 2026: 4.

91 papers.

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  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  2. 2022Transformers in Medical Imaging: A Surveycited by 245
  3. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  4. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  5. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  6. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  7. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  8. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
  9. 2022A Neural-Network-Based Convex Regularizer for Inverse Problemscited by 40
  10. 2021Memory-Augmented Deep Unfolding Network for Compressive Sensingcited by 38
  11. 2021AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysiscited by 38
  12. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
38 more
  1. 2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 19
  2. 2021Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRIcited by 14
  3. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  4. 2021Conditional Variational Autoencoder for Learned Image Reconstructioncited by 13
  5. 2022Conditional Injective Flows for Bayesian Imagingcited by 13
  6. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 13
  7. 2023On Retrospective k-space Subsampling schemes For Deep MRI Reconstructioncited by 12
  8. 2021Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgecited by 11
  9. 2022Adversarial Robustness of MR Image Reconstruction under Realistic Perturbationscited by 11
  10. 2023Uncertainty Estimation and Propagation in Accelerated MRI Reconstructioncited by 10
  11. 2023ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Spacecited by 10
  12. 2022Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priorscited by 9
  13. 2021MRI Reconstruction Using Deep Energy-Based Modelcited by 8
  14. 2021An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstructioncited by 7
  15. 2024MambaRecon: MRI Reconstruction with Structured State Space Modelscited by 7
  16. 2021Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructioncited by 6
  17. 2022Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spacescited by 6
  18. 2022PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRIcited by 5
  19. 2023Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problemscited by 5
  20. 2021ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Datacited by 4
  21. 2022Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstructioncited by 4
  22. 2022D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensingcited by 4
  23. 2022Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariancecited by 4
  24. 2023CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstructioncited by 4
  25. 2022MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolationcited by 3
  26. 2022JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRIcited by 3
  27. 2023Deep Injective Prior for Inverse Scatteringcited by 3
  28. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  29. 2023Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Studycited by 3
  30. 2023Electrical Impedance Tomography with Deep Calder'on Methodcited by 3
  31. 2023Fast MRI Reconstruction via Edge Attentioncited by 3
  32. 2024MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learningcited by 3
  33. 2025PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generationcited by 3
  34. 2021Deep MRI Reconstruction with Radial Subsamplingcited by 2
  35. 2021Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRIcited by 2
  36. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  37. 2022A Densely Interconnected Network for Deep Learning Accelerated MRIcited by 2
  38. 2022Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networkscited by 2

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