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

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

Recent theoretical advances in the field of compressive sampling—also referred to as
compressed sensing (CS)—hold considerable promise for practical applications in MRI, but …

MRI is one of the most dynamic and safe imaging techniques available in the clinic today.
However, MRI acquisitions tend to be slow, limiting patient throughput and limiting potential …

The family of sparse reconstruction techniques, including the recently introduced
compressed sensing framework, has been extensively explored to reduce scan times in …

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 …

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) holds considerable promise to accelerate the data acquisition in
magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal …

Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI).
Compressed sensing has recently emerged as a theoretical foundation for the …