Abstract

In k-space, low frequency components are distributed around the center and high frequency components are at the\nperiphery. These low and high frequency values represent the higher and lower energy samples respectively. The contrast of\nthe MR image is mainly due to the higher energy samples. Hence more number of low frequency components is acquired for\nproper image reconstruction. Large amount of samples are collected near the middle region than the outer boundary surface.\nThis process of collecting few data from which an image can be efficiently reconstructed is identified as Compressive\nSensing or Compressive Sampling (CS). To get the undersampled data or k-space data sampling trajectories are applied on\nthe fully sampled k-space. These sampling trajectories are generated using Probability Density Function (PDF). It is observed\nthat proper image reconstruction is possible with only 40% of k-space data.