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01/27/2022
Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
We benchmark the current existing methods to jointly learn non-Cartesian...
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08/06/2021
Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging
The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKL...
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06/01/2021
Is good old GRAPPA dead?
We perform a qualitative analysis of performance of XPDNet, a state-of-t...
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06/01/2021
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
In recent years, implicit deep learning has emerged as a method to incre...
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01/05/2021
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a pow...
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12/09/2020
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 t...
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11/16/2020
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Deep neural networks have proven extremely efficient at solving a wide r...
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10/15/2020
XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge
We present a modular cross-domain neural network the XPDNet and its appl...
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10/31/2019
Anisotropic compressed sensing for non-Cartesian MRI acquisitions
In the present note we develop some theoretical results in the theory of...
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09/06/2016
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
Principal component analysis (PCA) is an exploratory tool widely used in...
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12/23/2011
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of...
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03/17/2011