mlsanityData to 7 Oct 2026

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

arXiv 2203.03196 ↗First posted 7 Mar 2022eess.IV cs.CVcited by 5

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Abstract (arXiv)

Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc. This generally accessible appearance-related side information affects deep learning-based undersampled magnetic resonance imaging (MRI) reconstruction frameworks, but has been overlooked in the majority of current works. In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction. Specifically, a Side Information-Guided Normalisation (SIGN) module, containing only few layers, is proposed to efficiently encode the side information and output the normalisation parameters. We examine the effectiveness of such a module on two popular reconstruction architectures, D5C5 and OUCR. The experimental results on both brain and knee images under various acceleration rates demonstrate that the proposed method improves on its corresponding baseline architectures with a significant margin.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 7 Mar 2022.

Builds on

19 of 33 references.

References · 19

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  2. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
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7 more
  1. 2021Task Transformer Network for Joint MRI Reconstruction and Super-Resolutioncited by 58
  2. 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
  3. 2019GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstructioncited by 45
  4. 2020Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problemscited by 38
  5. 2022DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstructioncited by 30
  6. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  7. 2021Universal Undersampled MRI Reconstructioncited by 15

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 1.

5 papers.

  1. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  2. 2023Unified Multi-Modal Image Synthesis for Missing Modality Imputationcited by 39
  3. 2023Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstructioncited by 2
  4. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2
  5. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1