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.…
Category: publications_COSMIC
Publications related to the COSMIC project.
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI…
State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Accelerating MRI scans is one of the principal outstanding problems in the MRI research…
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…
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide range of inverse…
Wavelets in the Deep Learning Era
Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years…
Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset
The MRI reconstruction field lacked a proper data set that allowed for reproducible…
Benchmarking proximal methods acceleration enhancements for CS-acquired MR image analysis reconstruction
Reference: Z. Ramzi, P. Ciuciu and J.-L. Starck. “Benchmarking proximal methods acceleration enhancements for CS-acquired MR image…
PySAP: Python Sparse Data Analysis Package for Multidisciplinary Image Processing
Authors: S. Farrens, A. Grigis, L. El Gueddari, Z. Ramzi, Chaithya G. R.,…
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging…