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

Most current approaches to undersampled multi-coil MRI reconstruction focus on learning
the reconstruction model for a fixed, equidistant acquisition trajectory. In this paper, we study …

Abstract Magnetic Resonance Image (MRI) acquisition is an inherently slow process which
has spurred the development of two different acceleration methods: acquiring multiple …

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 …

Current deep learning-based reconstruction models for accelerated multi-coil magnetic
resonance imaging (MRI) mainly focus on subsampled k-space data of single modality using …

Deep learning methods have achieved attractive performance in dynamic MR cine imaging.
However, most of these methods are driven only by the sparse prior of MR images, while the …

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 …

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 …

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 …

The slow acquisition speed of magnetic resonance imaging (MRI) has led to the
development of two complementary methods: acquiring multiple views of the anatomy …