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Research Article |

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

Author(s): Moureen Nalumansi*, Elijah Mwangi and George Kamucha

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 2

Publisher : FOREX Publication

Published : 30 April 2024

e-ISSN : 2347-470X

Page(s) : 393-402

DOI: https://doi.org/10.37391/IJEER.120209

Abstract

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

Keywords: Compressed sensing, Dual Tree Complex Wavelet Transform, Particle Swarm Optimization, Magnetic Resonance Imaging.




Moureen Nalumansi*, Department of Electrical Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya; Email: moureen.nalumansi@students.jkuat.ac.ke

Elijah Mwangi, Faculty of Engineering, University of Nairobi, Kenya; Email: elijah.mwangi@uonbi.ac.ke

George Kamucha, Faculty of Engineering, University of Nairobi, Kenya; Email: gkamucha@uonbi.ac.ke

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Moureen Nalumansi, Elijah Mwangi and George Kamucha (2024), Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm. IJEER 12(2), 393-402. DOI: 10.37391/IJEER.120209.