XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge

Zaccharie Ramzi (1, 2, 3) , Philippe Ciuciu (1, 2) , Jean-Luc Starck (3, 4)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 NEUROSPIN - Service NEUROSPIN
3 LCS - COSMOSTAT - Laboratoire de Cosmologie et Statistiques
4 AIM (UMR_7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation

We present a modular cross-domain neural network the XPDNet and its application to the MRI reconstruction task. This approach consists in unrolling the PDHG algorithm as well as learning the acceleration scheme between steps. We also adopt state-of-the-art techniques specific to Deep Learning for MRI reconstruction. At the time of writing, this approach is the best performer in PSNR on the fastMRI leaderboards for both knee and brain at acceleration factor 4.

Domaines: Physique Médicale [physics.med-ph]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Machine Learning [stat.ML]; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]

Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck. XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge. 2020. ⟨hal-03020202⟩