Abstract
Diagnosis of diseases require high resolution images of human body parts. Magnetic Resonance Imaging (MRI) is a popular technology commonly used for this purpose. In addition to having several benefits, this technology has few shortcomings also. One of them is its high scanning time. In MRI acquisition of image is based on the principle of traditional sampling theorem. The novel sampling theory called as Compressive Sensing (CS) which allows the reconstruction of sparse signals from undersampled data. The application of CS onto MRI will drastically reduce the acquisition time and hence scanning time. In this manuscript analysis and application of CS on to MRI is demonstrated. Simulations are carried out using Variable Density Sampling trajectories (VDS). Then a comparative study is made in terms of Signal to Noise Ratio (SNR) and execution time based on the result obtained.
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