Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images
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.