# MRI Reconstruction

91 papers with code • 5 benchmarks • 4 datasets

In its most basic form, MRI reconstruction consists in retrieving a complex-valued image from its under-sampled Fourier coefficients. Besides, it can be addressed as a encoder-decoder task, in which the normative model in the latent space will only capture the relevant information without noise or corruptions. Then, we decode the latent space in order to have a reconstructed MRI.

## Libraries

Use these libraries to find MRI Reconstruction models and implementations## Most implemented papers

# Homotopic Gradients of Generative Density Priors for MR Image Reconstruction

Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.

# End-to-End Variational Networks for Accelerated MRI Reconstruction

The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).

# Accelerated MRI with Un-trained Neural Networks

Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems.

# Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction

Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.

# Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.

# Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data

Results: Results on five different knee sequences at acceleration rate of 4 shows that proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study.

# 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.

# Learning Multiscale Convolutional Dictionaries for Image Reconstruction

To close the performance gap, we thus propose a multiscale convolutional dictionary structure.

# 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.

# Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks

In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic reconstruction model.