313 citations · 490 across the 26 of their papers we have counts for
29 papers
SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back
Pierre-Antoine Comby, Alexandre Vignaud, Philippe Ciuciu
We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling…
Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
Chaithya G R, Philippe Ciuciu
We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently…
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…