mlsanityData to 7 Oct 2026

ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer

arXiv 2201.09376 ↗First posted 23 Jan 2022eess.IV cs.CVcited by 52

Not on the map. Why?

Abstract (arXiv)

Accelerating magnetic resonance image (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space. In this paper, we propose a recurrent transformer model, namely ReconFormer, for MRI reconstruction which can iteratively reconstruct high fertility magnetic resonance images from highly under-sampled k-space data. In particular, the proposed architecture is built upon Recurrent Pyramid Transformer Layers (RPTL), which jointly exploits intrinsic multi-scale information at every architecture unit as well as the dependencies of the deep feature correlation through recurrent states. Moreover, the proposed ReconFormer is lightweight since it employs the recurrent structure for its parameter efficiency. We validate the effectiveness of ReconFormer on multiple datasets with different magnetic resonance sequences and show that it achieves significant improvements over the state-of-the-art methods with better parameter efficiency. Implementation code will be available in https://github.com/guopengf/ReconFormer.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). Builds on 39 of its 69 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

39 of 69 references.

References · 39

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2020End-to-End Object Detection with Transformerscited by 7,384
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  8. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  9. 2021Multiscale Vision Transformerscited by 896
  10. 2020Pre-Trained Image Processing Transformercited by 891
  11. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  12. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 549
27 more
  1. 2021Early Convolutions Help Transformers See Bettercited by 395
  2. 2021Score-based diffusion models for accelerated MRIcited by 308
  3. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  4. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  5. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
  6. 2021Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encodingcited by 207
  7. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  8. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  9. 2018Non-Local Recurrent Network for Image Restorationcited by 160
  10. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  11. 2021Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformerscited by 76
  12. 2020DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Priorcited by 72
  13. 2021Task Transformer Network for Joint MRI Reconstruction and Super-Resolutioncited by 58
  14. 2022Swin Transformer for Fast MRIcited by 56
  15. 2020KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representationscited by 49
  16. 2017A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networkscited by 43
  17. 2021Multi-Modal Transformer for Accelerated MR Imagingcited by 42
  18. 2017Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MRcited by 31
  19. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  20. 2017ADMM-Net: A Deep Learning Approach for Compressive Sensing MRIcited by 28
  21. 2019Invert to Learn to Invertcited by 18
  22. 2019i-RIM applied to the fastMRI challengecited by 16
  23. 2019Pyramid Convolutional RNN for MRI Image Reconstructioncited by 14
  24. 2020MRI Image Reconstruction via Learning Optimization Using Neural ODEscited by 14
  25. 2020Homotopic Gradients of Generative Density Priors for MR Image Reconstructioncited by 14
  26. 2017Deep artifact learning for compressed sensing and parallel MRIcited by 5
  27. 2021Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformercited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 20; 2025: 14; 2026: 6.

52 papers.

Sort:

By area:

  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  2. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  3. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  4. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  5. 2023Transformers in Healthcare: A Surveycited by 17
  6. 2024MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusioncited by 15
  7. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 13
  8. 2024Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRIcited by 13
  9. 2024Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstructioncited by 9
  10. 2024MambaRecon: MRI Reconstruction with Structured State Space Modelscited by 7
  11. 2024Physics-Driven Autoregressive State Space Models for Medical Image Reconstructioncited by 7
  12. 2025Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Priorcited by 7
38 more
  1. 2024Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRIcited by 6
  2. 2024Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversificationcited by 6
  3. 2022Undersampled MRI Reconstruction with Side Information-Guided Normalisationcited by 5
  4. 2024Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Modelscited by 5
  5. 2023CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstructioncited by 4
  6. 2024MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion modelcited by 3
  7. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  8. 2025DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstructioncited by 3
  9. 2025Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systemscited by 3
  10. 2025HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstructioncited by 3
  11. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  12. 2024End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRIcited by 2
  13. 2025Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representationscited by 2
  14. 2025Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Modelscited by 2
  15. 2023Computationally Efficient 3D MRI Reconstruction with Adaptive MLPcited by 1
  16. 2023A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningcited by 1
  17. 2023Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive fieldcited by 1
  18. 2024DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstructioncited by 1
  19. 2024SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attentioncited by 1
  20. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  21. 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstructioncited by 1
  22. 2025PrIINeR: Towards Prior-Informed Implicit Neural Representations for Accelerated MRIcited by 1
  23. 2025HiFi-MambaV2: Hierarchical Shared-Routed MoE for High-Fidelity MRI Reconstructioncited by 1
  24. 2025SDUM: A Scalable Deep Unrolled Model for Universal MRI Reconstructioncited by 1
  25. 2023Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Datacited by 0
  26. 2023Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representationcited by 0
  27. 2024Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approachcited by 0
  28. 2024Accelerated Proton Resonance Frequency-based Magnetic Resonance Thermometry by Optimized Deep Learning Methodcited by 0
  29. 2024TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scalingcited by 0
  30. 2024LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Priorcited by 0
  31. 2024A Learned Proximal Alternating Minimization Algorithm and Its Induced Network for a Class of Two-block Nonconvex and Nonsmooth Optimizationcited by 0
  32. 2025Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integrationcited by 0
  33. 2025Towards Globally Predictable k-Space Interpolation: A White-box Transformer Approachcited by 0
  34. 2025FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstructioncited by 0
  35. 2026PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstructioncited by 0
  36. 2026Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselinescited by 0
  37. 2026Next-Acceleration-Scale Prediction for Autoregressive MRI Reconstructioncited by 0
  38. 2026SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstructioncited by 0

50 most cited of 52 All 52 (JSON)