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XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge
Zaccharie Ramzi1,2,3, Jean-Luc Starck2, and Philippe Ciuciu1,3
1Neurospin, Gif-Sur-Yvette, France, 2Cosmostat team, CEA, Gif-Sur-Yvette, France, 3Parietal team, Inria Saclay, Gif-Sur-Yvette, France
We introduce the XPDNet, a neural network for MRI reconstruction, which ranked second in the fastMRI 2020 challenge.
General cross-domain networks architecture. Skip and residual connection are omitted for the sake of clarity. y are the under-sampled measurements,in our case the k-space measurements, Ω is the under-sampling scheme,Fis the measurement operator, in our case the Fourier Transform, and x is the recovered solution.
Magnitude reconstruction results for the different fastMRI contrasts at acceleration factor 4. The top row represents the ground truth, the middle one represents the reconstruction from a retrospectively under-sampled k-space,and the bottom row represents the absolute error when comparing the two. The average image quantitative metrics are given for 30 validation volumes.