Search Results for author: Philippe Ciuciu

Found 15 papers, 9 papers with code

SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back

1 code implementation12 Apr 2024 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 to \emph{A}cquisition of \emph{K}-space data for \emph{E}xploration of fMRI acquisition techniques. Unlike existing tools, the goal here is to simulate the complete chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various multi-coil k-space data 3D sampling strategies, with the possibility of extending the forward acquisition model to various noise and artifact sources while remaining memory-efficient. By using this \emph{in silico} setup, we are thus able to provide realistic and reproducible ground truth for fMRI reconstruction methods in 3D accelerated acquisition settings and explore the influence of critical parameters, such as the acceleration factor and signal-to-noise ratio~(SNR), on downstream tasks of image reconstruction and statistical analysis of evoked brain activity. We present three scenarios of increasing complexity to showcase the flexibility, versatility, and fidelity of \emph{SNAKE-fMRI}: From a temporally-fixed full 3D Cartesian to various 3D non-Cartesian sampling patterns, we can compare -- with reproducibility guarantees -- how experimental paradigms, acquisition strategies and reconstruction methods contribute and interact together, affecting the downstream statistical analysis.

Image Reconstruction

Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks

no code implementations27 Jan 2022 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 developed generalized hybrid learning (HybLearn) framework.


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

Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty

no code implementations6 Sep 2016 Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay

However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population.

Data-driven HRF estimation for encoding and decoding models

no code implementations27 Feb 2014 Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu, Bertrand Thirion, Alexandre Gramfort

We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.

Computational Efficiency

Variable density sampling with continuous trajectories. Application to MRI

1 code implementation23 Nov 2013 Nicolas Chauffert, Philippe Ciuciu, Jonas Kahn, Pierre Weiss

Its standard design relies on the random drawing of independent measurements.


Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

no code implementations23 Dec 2011 Lotfi Chaari, Sébastien Mériaux, Jean-Christophe Pesquet, Philippe Ciuciu

To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated.

MRI Reconstruction

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