Electroencephalography (EEG) is a non-invasive measurement method for brain activity.
Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural …

Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …

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 …

Purpose The radial k‐space trajectory is a well‐established sampling trajectory used in
conjunction with magnetic resonance imaging. However, the radial k‐space trajectory …

The sparsity which is implicit in MR images is exploited to significantly undersample k‐
space. Some MR images such as angiograms are already sparse in the pixel …

This article reviews the requirements for successful compressed sensing (CS), describes
their natural fit to MRI, and gives examples of four interesting applications of CS in MRI. The …

Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate
conventional MRI by reducing the number of acquired data. The combination of parallel MRI …

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of
potential benefits ranging from high-temporal-rate observation of physiological processes to …

In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm
minimizes a linear combination of three terms corresponding to a least square data fitting …

Compressed sensing, an emerging multidisciplinary field involving mathematics, probability,
optimization, and signal processing, focuses on reconstructing an unknown signal from a …