Search Results for author: Zaccharie Ramzi

Found 11 papers, 8 papers with code

Test like you Train in Implicit Deep Learning

no code implementations24 May 2023 Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré, Thomas Moreau

Implicit deep learning has recently gained popularity with applications ranging from meta-learning to Deep Equilibrium Networks (DEQs).

Meta-Learning

Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks

no code implementations25 Oct 2021 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 reconstruction from the undersampled k-space data.

Image Reconstruction SSIM

Is good old GRAPPA dead?

2 code implementations1 Jun 2021 Zaccharie Ramzi, Alexandre Vignaud, Jean-Luc Starck, Philippe Ciuciu

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.

MRI Reconstruction

SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models

2 code implementations ICLR 2022 Zaccharie Ramzi, Florian Mannel, Shaojie Bai, Jean-Luc Starck, Philippe Ciuciu, Thomas Moreau

In Deep Equilibrium Models (DEQs), the training is performed as a bi-level problem, and its computational complexity is partially driven by the iterative inversion of a huge Jacobian matrix.

Hyperparameter Optimization

Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction

no code implementations5 Mar 2021 Chaithya G R, Zaccharie Ramzi, Philippe Ciuciu

However, the two main limitations of SPARKLING are first that the optimal target sampling density is unknown and thus a user-defined parameter and second that this sampling pattern generation remains disconnected from MR image reconstruction thus from the optimization of image quality.

Image Reconstruction

Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

1 code implementation5 Jan 2021 Zaccharie Ramzi, Jean-Luc Starck, Philippe Ciuciu

Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction.

MRI Reconstruction

Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

1 code implementation16 Nov 2020 Zaccharie Ramzi, Benjamin Remy, Francois Lanusse, Jean-Luc Starck, Philippe Ciuciu

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.

Denoising MRI Reconstruction +1

XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge

3 code implementations15 Oct 2020 Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck

We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data.

MRI Reconstruction

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