A Characterization of CS-MRI Reconstruction Using PSO for Random Under Sampling Pattern

Abstract

MR imaging method is widely used for diagnosis applications. The echo signal received from the MR scanning machine is used to generate the image. The data acquisition and reconstruction are the important operations. In this paper the kspace is compressively sampled using Radial Sampling pattern for acquiring the k-space data and Particle Swarm Optimization (PSO) with Total Variation (TV) is used as the reconstruction algorithm for the faithful reconstruction of MR image. The experiments are conducted on MR images of Brain, Head Angiogram and Shoulder images. Performance of the proposed method of reconstruction is analyzed for different sampling kspace scanning percentages. The reconstruction results are compared with the standard sampling pattern used for compressive sampling prove the novelty of the proposed method. The results are verified in terms of Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and Structural Similarity index (SSIM).