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 …

Compressed sensing (CS) is a mathematical framework that reconstructs data from highly
undersampled measurements. To gain acceleration in acquisition time, CS has been …

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 …

Compressed sensing (CS) has the potential to reduce magnetic resonance (MR) data
acquisition time. In order for CS-based imaging schemes to be effective, the signal of interest …

Compressed sensing (CS) has recently drawn great attentions in the MRI research
community. The most desirable property of CS in MRI application is that it allows sampling of …

MRI is often the most sensitive or appropriate technique for important measurements in
clinical diagnosis and research, but lengthy acquisition times limit its use due to cost and …

Compressed Sensing (CS) enables magnetic resonance imaging (MRI) at high
undersampling by exploiting the sparsity of MR images in a certain transform domain or …

The introduction of compressed sensing for increasing imaging speed in magnetic
resonance imaging (MRI) has raised significant interest among researchers and clinicians …

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 …

Compressed Sensing (CS) takes advantage of the sparsity of MR images in certain bases or
dictionaries to obtain accurate reconstructions from undersampled k-space data. The …