Purpose To advance research in the field of machine learning for MR image reconstruction
with an open challenge. Methods We provided participants with a dataset of raw k‐space …

Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled
data is important in many clinical applications. In recent years, deep learning-based …

Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …

Accelerating MRI scans is one of the principal outstanding problems in the MRI research
community. Towards this goal, we hosted the second fastMRI competition targeted towards …

Convolutional Neural Networks (CNNs) are highly effective for image reconstruction
problems. Typically, CNNs are trained on large amounts of training images. Recently …

Deep neural networks give state-of-the-art accuracy for reconstructing images from few and
noisy measurements, a problem arising for example in accelerated magnetic resonance …

Purpose To systematically investigate the influence of various data consistency layers and
regularization networks with respect to variations in the training and test data domain, for …

Deep neural networks have emerged as very successful tools for image restoration and
reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an …

The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …