English

XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge

Image and Video Processing 2021-07-08 v2 Computer Vision and Pattern Recognition Machine Learning Medical Physics Machine Learning
Authors: Zaccharie Ramzi , Philippe Ciuciu , Jean-Luc Starck
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Abstract

We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data. We inform the design of this network by taking best practices from MRI reconstruction and computer vision. We show that this network can achieve state-of-the-art reconstruction results, as shown by its ranking of second in the fastMRI 2020 challenge.

Keywords

MRI reconstruction XPDNet under-sampled multi-coil data fastMRI challenge state-of-the-art neural network

Cite

@article{arxiv.2010.07290,
  title  = {XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge},
  author = {Zaccharie Ramzi and Philippe Ciuciu and Jean-Luc Starck},
  journal= {arXiv preprint arXiv:2010.07290},
  year   = {2021}
}

Comments

8 pages, 3 figures, presented as an oral to the 2021 ISMRM conference

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