  Magnetic resonance imaging (MRI) is an essential medical tool with inherently
slow data acquisition process. Slow acquisition process requires patient to be
long time exposed to scanning apparatus. In recent years significant efforts
are made towards the applying Compressive Sensing technique to the acquisition
process of MRI and biomedical images. Compressive Sensing is an emerging theory
in signal processing. It aims to reduce the amount of acquired data required
for successful signal reconstruction. Reducing the amount of acquired image
coefficients leads to lower acquisition time, i.e. time of exposition to the
MRI apparatus. Using optimization algorithms, satisfactory image quality can be
obtained from the small set of acquired samples. A number of optimization
algorithms for the reconstruction of the biomedical images is proposed in the
literature. In this paper, three commonly used optimization algorithms are
compared and results are presented on the several MRI images.
