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Found 11 papers, 8 papers with code
Test like you Train in Implicit Deep Learning
Implicit deep learning has recently gained popularity with applications ranging from meta-learning to Deep Equilibrium Networks (DEQs).
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks
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.
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.
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
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.
Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction
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.
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction
Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
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.
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.
Ranked #2 on
MRI Reconstruction
on fastMRI Brain 8x
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI).
