mlsanityData to 7 Oct 2026

Is good old GRAPPA dead?

arXiv 2106.00753 ↗First posted 1 Jun 2021eess.IV cs.LG physics.med-phcited by 0

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Abstract (arXiv)

We perform a qualitative analysis of performance of XPDNet, a state-of-the-art deep learning approach for MRI reconstruction, compared to GRAPPA, a classical approach. We do this in multiple settings, in particular testing the robustness of the XPDNet to unseen settings, and show that the XPDNet can to some degree generalize well.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 1 Jun 2021.

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8 of 13 references.

References · 8

  1. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  2. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  3. 2017Image reconstruction by domain transform manifold learningcited by 231
  4. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  5. 2018Multi-level Wavelet-CNN for Image Restorationcited by 184
  6. 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
  7. 2020Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstructioncited by 14
  8. 2016Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choicescited by 1

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