Compressive sensing is the reconstruction of sparse images or signals from very few
samples, by means of solving a tractable optimization problem. In the context of MRI, this …

A major problem in imaging applications such as magnetic resonance imaging and synthetic
aperture radar is the task of trying to reconstruct an image with the smallest possible set of …

The theory of compressive sensing has shown that sparse signals can be reconstructed
exactly from many fewer measurements than traditionally believed necessary. In [1], it was …

The recently emerged field known as compressive sensing has produced powerful results
showing the ability to recover sparse signals from surprisingly few linear measurements …

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

Compressive sensing is a new type of sampling theory, which predicts that sparse signals
and images can be reconstructed from what was previously believed to be incomplete …

Compressed sensing or compressive sampling (CS) has been receiving a lot of interest as a
promising method for signal recovery and sampling. CS problems can be cast as convex …

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 …

Recent compressive sensing results show that it is possible to accurately reconstruct certain
compressible signals from relatively few linear measurements via solving nonsmooth …

In recent years, there has been a concerted effort to reduce the MR scan time. Signal
processing research aims at reducing the scan time by acquiring less K-space data. The …