A Characterization of CS-MRI Reconstruction Using PSO for Random Under Sampling Pattern
G. Shrividya1, S.H. Bharathi2

1G.Shrividya, Research Scholar, Department of E&C Engineering, REVA University, NMAMIT, Nitte, India.
2S.H. Bharathi, Department of E&C Engineering, REVA University, Bengaluru (Karnataka), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 376-381 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10580982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1058.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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).
Keywords: CS-MRI, Random Sampling Pattern, Compressed Sampling, Sampling Trajectory, PSO-TV.
Scope of the Article: Software Design Patterns