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Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

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 setting of MRI, namely non-Cartesian acquisitions. In this work, we …

Is good old GRAPPA dead?

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 in multiple settings, in particular testing the robustness of the …

Results of the 2020 fastMRI Brain Reconstruction Challenge

The next round of the fastMRI reconstruction challenge took place, this time using anatomical brain data. Submissions were ranked by SSIM and resulting ?nalists again by 6 radiologists. We observed the cases with clear SSIM separation achieving the …

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

We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data. We inform the design of this network by taking best practices from MRI reconstruction and computer vision. We show that this network …

Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset

The MRI reconstruction field lacked a proper data set that allowed for reproducible results on real raw data (i.e. complex-valued), especially when it comes to deep learning (DL) methods as this kind of approaches require much more data than …

Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

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. In this work, we propose a generic Bayesian framework forsolving inverse …

Probabilistic Mapping of Dark Matter by Neural Score Matching

The Dark Matter present in the Large-Scale Structure of the Universe is invisible, but its presence can be inferred through the small gravitational lensing effect it has on the images of far away galaxies. By measuring this lensing effect on a large …

Wavelets in the deep learning era

Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years for inverse problems before being overtaken by neural networks. In particular, U-nets have proven to be extremely effective. Their main ingredients are a …

Benchmarking proximal methods acceleration enhancements for CS-acquired MR image analysis reconstruction