mlsanityData to 14 Oct 2026

GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

arXiv 1910.12325 ↗First posted 27 Oct 2019eess.IV cs.CVcited by 45

Not on the map. Why?

Abstract (arXiv)

Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating the speed of MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both 4× and 8× acceleration.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Oct 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 17; 2022: 10; 2023: 10; 2024: 4; 2025: 3; 2026: 1.

45 papers.

Sort:

By area:

  1. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  2. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model samplingcited by 60
  3. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 50
  4. 2021Multi-Modal Transformer for Accelerated MR Imagingcited by 43
  5. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 38
  6. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  7. 2021One-dimensional Deep Low-rank and Sparse Network for Accelerated MRIcited by 27
  8. 2021Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimizationcited by 19
  9. 2021Universal Undersampled MRI Reconstructioncited by 15
  10. 2021A review and experimental evaluation of deep learning methods for MRI reconstructioncited by 13
  11. 2021Dual-Octave Convolution for Accelerated Parallel MR Image Reconstructioncited by 12
  12. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 12
33 more
  1. 2023On Retrospective k-space Subsampling schemes For Deep MRI Reconstructioncited by 12
  2. 2021Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstructioncited by 11
  3. 2021MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networkscited by 10
  4. 2021An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivitiescited by 10
  5. 2021DONet: Dual-Octave Network for Fast MR Image Reconstructioncited by 9
  6. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  7. 2022PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imagingcited by 8
  8. 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
  9. 2022Undersampled MRI Reconstruction with Side Information-Guided Normalisationcited by 5
  10. 2022Invertible Sharpening Network for MRI Reconstruction Enhancementcited by 5
  11. 2023Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noisecited by 4
  12. 2024NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimationcited by 4
  13. 2021Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructioncited by 3
  14. 2022JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRIcited by 3
  15. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3
  16. 2021Deep MRI Reconstruction with Radial Subsamplingcited by 2
  17. 2021Deep multi-modal aggregation network for MR image reconstruction with auxiliary modalitycited by 2
  18. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  19. 2023Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networkscited by 2
  20. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2
  21. 2024Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priorscited by 2
  22. 2021Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprintingcited by 1
  23. 2022Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstructioncited by 1
  24. 2024Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstructioncited by 1
  25. 2024Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstructioncited by 1
  26. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  27. 2025Efficient Noise Calculation in Deep Learning-based MRI Reconstructionscited by 1
  28. 2021Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networkscited by 0
  29. 2023Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representationcited by 0
  30. 2023k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstructioncited by 0
  31. 2023Investigating the use of publicly available natural videos to learn Dynamic MR image reconstructioncited by 0
  32. 2025Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integrationcited by 0
  33. 2026UEPS: Robust and Efficient MRI Reconstructioncited by 0