A study of Optimum Sampling Pattern for Reconstruction of MR Images using Compressive Sensing

Abstract

Magnetic Resonance (MR) imaging is a non invasive medical imaging technique used widely for diagnosis. The data collected by MRI scanner is placed in the k-space. Various algorithms are developed to sample the k-space and reconstruct the image from the compressive sampled k-space data. The k-space sampling pattern plays an important role in optimizing compressed sensing magnetic resonance imaging. CS technique violates the Nyquist’s sampling theory by sampling signals at lower rate than conventional sampling rate. CS can reduce scanning time in MRI applications by acquiring very few samples. This paper analyses the Cartesian variable density k-space data sampling pattern with the radial sampling scheme. Qualitative and quantitative analysis are performed on the reconstructed MR Image for different sampling percentages.