313 citations · 490 across the 26 of their papers we have counts for
6 papers · 1 filter
Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks
Chaithya G R, Zaccharie Ramzi, Philippe Ciuciu
Compressed sensing (CS) in Magnetic resonance Imaging (MRI) essentially involves the optimization of 1) the sampling pattern in k-space under MR hardware constraints and 2) image r…
Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging
Chaithya G R, Pierre Weiss, Guillaume Daval-Frérot +3
The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using c…
Is good old GRAPPA dead?
Zaccharie Ramzi, Alexandre Vignaud, Jean-Luc Starck +1
We perform a qualitative analysis of performance of XPDNet, a state-of-the-art deep learning approach for MRI reconstruction, compared to GRAPPA, a classical approach. We do this i…
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
Zaccharie Ramzi, Florian Mannel, Shaojie Bai +3
In recent years, implicit deep learning has emerged as a method to increase the effective depth of deep neural networks. While their training is memory-efficient, they are still si…
Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction
Chaithya G R, Zaccharie Ramzi, Philippe Ciuciu
The SPARKLING algorithm was originally developed for accelerated 2D magnetic resonance imaging (MRI) in the compressed sensing (CS) context. It yields non-Cartesian sampling trajec…
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Zaccharie Ramzi, Jean-Luc Starck, Philippe Ciuciu
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…