mlsanityData to 7 Oct 2026

Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data

arXiv 2012.08931 ↗First posted 15 Dec 2020eess.IVcited by 24

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Abstract (arXiv)

Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed". Reducing the scanned measurements can accelerate MR imaging with the aid of powerful reconstruction methods, which have evolved from linear analytic models to nonlinear iterative ones. The emerging trend in this area is replacing human-defined signal models with that learned from data. Specifically, from 2016, deep learning has been incorporated into the fast MR imaging task, which draws valuable prior knowledge from big datasets to facilitate accurate MR image reconstruction from limited measurements. This survey aims to review deep learning based MR image reconstruction works from 2016- June 2020 and will discuss merits, limitations and challenges associated with such methods. Last but not least, this paper will provide a starting point for researchers interested in contributing to this field by pointing out good tutorial resources, state-of-the-art open-source codes and meaningful data sources.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Dec 2020.

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Citing papers per year: 2021: 2; 2022: 8; 2023: 4; 2024: 6; 2025: 3; 2026: 1.

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  1. 2021Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Frameworkcited by 26
  2. 2022PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imagingcited by 8
  3. 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
  4. 2021Learned Half-Quadratic Splitting Network for MR Image Reconstructioncited by 5
  5. 2022An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillationcited by 5
  6. 2022SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imagingcited by 5
  7. 2022Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstructioncited by 4
  8. 2023CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstructioncited by 4
  9. 2023PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classificationcited by 3
  10. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
  11. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  12. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
12 more
  1. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  2. 2024RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representationcited by 2
  3. 2022Iterative Data Refinement for Self-Supervised MR Image Reconstructioncited by 1
  4. 2023AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRIcited by 1
  5. 2024Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imagingcited by 1
  6. 2024SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDIcited by 1
  7. 2026Efficient Unrolled Networks for Large-Scale 3D Inverse Problemscited by 1
  8. 2022A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstructioncited by 0
  9. 2024Real-time cardiac cine MRI -- A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitionscited by 0
  10. 2025Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integrationcited by 0
  11. 2025Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scanscited by 0
  12. 2025Simultaneous Image Quality Improvement and Artefacts Correction in Accelerated MRIcited by 0