Adaptive intelligence aims at empowering machine learning techniques with the additional
use of domain knowledge. In this work, we present the application of adaptive intelligence to …

Deep learning has shown astonishing performance in accelerated magnetic resonance
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …

Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial
recent improvement combining compressed sensing with deep learning. However, most of …

Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method
in clinical medicine, but it has always suffered from the problem of long acquisition time …

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 …

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical
foundations upon which the time-consuming MRI acquisition process can be accelerated …

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which
is highly desirable for numerous clinical applications. This can not only reduce the scanning …

In this work, we propose a deep learning approach for parallel magnetic resonance imaging
(MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high …

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 …

Most deep learning (DL) magnetic resonance imaging (MRI) reconstruction approaches rely
on supervised training algorithms, which require access to high-quality, fully-sampled …