Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Purpose To advance research in the field of machine learning for MR image reconstruction
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
MJ Muckley, B Riemenschneider… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Accelerating MRI scans is one of the principal outstanding problems in the MRI research
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
Noise2Recon: Enabling SNR‐robust MRI reconstruction with semi‐supervised and self‐supervised learning
Purpose To develop a method for building MRI reconstruction neural networks robust to
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …
NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction
Deep Learning has become a very promising avenue for magnetic resonance image (MRI)
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …
reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian …
[PDF][PDF] State-of-the-art machine learning MRI reconstruction in 2020: Results of the second fastMRI challenge
MJ Muckley, B Riemenschneider… - arXiv preprint arXiv …, 2020 - hal.science
Accelerating MRI scans is one of the principal outstanding problems in the MRI research
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
Deep unfolding with normalizing flow priors for inverse problems
X Wei, H Van Gorp… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Many application domains, spanning from computational photography to medical imaging,
require recovery of high-fidelity images from noisy, incomplete or partial/compressed …
require recovery of high-fidelity images from noisy, incomplete or partial/compressed …
XPDNet for MRI reconstruction: an application to the fastMRI 2020 brain challenge
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 …
MRI reconstruction task. This approach consists in unrolling the PDHG algorithm as well as …
Density compensated unrolled networks for non-cartesian MRI reconstruction
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI
reconstruction. There is a lack of research, however, regarding their use for a specific setting …
reconstruction. There is a lack of research, however, regarding their use for a specific setting …
Denoising score-matching for uncertainty quantification in inverse problems
Deep neural networks have proven extremely efficient at solving a wide rangeof inverse
problems, but most often the uncertainty on the solution they provideis hard to quantify. In …
problems, but most often the uncertainty on the solution they provideis hard to quantify. In …
XPDNet for MRI reconstruction: An application to the 2020 fastMRI challenge
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 …
under-sampled multi-coil data. We inform the design of this network by taking best practices …