# Is good old GRAPPA dead?

arXiv: https://arxiv.org/abs/2106.00753 · HTML: https://mlsanity.com/paper/2106.00753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [End-to-End Variational Networks for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2004.06688/) (2020)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction](https://mlsanity.com/paper/1703.00555/) (2017)
- [Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction](https://mlsanity.com/paper/2012.06318/) (2020)
- [Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices](https://mlsanity.com/paper/1605.02559/) (2016)

## 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.