A practical under-sampling pattern for compressed sensing mri

Abstract

Typically, magnetic resonance (MR) images are stored in k-space where the higher energy samples, i.e., the samples with maximum information are concentrated near the center only; whereas, relatively lower energy samples are present near the outer periphery. Recently, variable density (VD) random under-sampling patterns have been increasingly popular and a topic of active research in compressed sensing (CS)-based MR image reconstruction. In this paper, we demonstrate a simple approach to design an efficient k-space under-sampling pattern, namely, the VD Poisson Disk (VD-PD) for sampling MR images in k-space and then implementing the same for CS-MRI reconstruction. Results are also compared with those obtained from some of the most prominent and commonly used sampling patterns, including the VD random with estimated PDF (VD-PDF), the VD Gaussian density (VD-Gaus), the VD uniform random (VD-Rnd), and the Radial Type in the CS-MRI literature.