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 …

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

Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR
data by learning a variational network that combines the mathematical structure of …

The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …

Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-
posed inverse problems are typically regularized using prior knowledge of the sought-after …

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear
reconstruction algorithms has been proposed which can be used with general Fourier …

Abstract Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …

Parallel magnetic resonance imaging has served as an effective and widely adopted
technique for accelerating data collection. The advent of sparse sampling offers aggressive …

Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been
traditionally limited by its slow data acquisition. Recent advances in compressed sensing …

Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however,
suffers from a long acquisition time. Sparse sampling effectively saves this time but images …