XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge
Published in
(2020)External Links:
TL
TL;DR
A new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data is presented, which can achieve state-of-the-art reconstruction results, as shown by its ranking of second in the fastMRI 2020 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.
References16 items
1
State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
2
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
3
End-to-End Variational Networks for Accelerated MRI Reconstruction
4
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
5