Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …

Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without
harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of …

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL)
methodology for continuous representation of measurements. Unlike traditional DL methods …

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …

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

Structured low-rank (SLR) algorithms, which exploit annihilation relations between the
Fourier samples of a signal resulting from different properties, is a powerful image …

In this study, a fast data-driven optimization approach, named bias-accelerated subset
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …

Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance
Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard …

Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal
algorithms, for example, the alternating direction method of multipliers (ADMM), with …

The proliferation of machine learning (ML) has drawn unprecedented interest in the study of
various multimedia contents such as text, image, audio, and video, among others …