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3 months ago

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

Zaccharie Ramzi Philippe Ciuciu Jean-Luc Starck

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

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.

Code Repositories

wdika/mridc
pytorch
Mentioned in GitHub
f78bono/deep-cine-cardiac-mri
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
mri-reconstruction-on-fastmri-brain-4xXPDNet
PSNR: 41.3
SSIM: 0.9581
mri-reconstruction-on-fastmri-brain-8xXPDNet
PSNR: 38.1
SSIM: 0.9408
mri-reconstruction-on-fastmri-knee-4xXPDNet
PSNR: 40.2
SSIM: 0.9287
mri-reconstruction-on-fastmri-knee-8xXPDNet
PSNR: 37.2
SSIM: 0.8893

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