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Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Review Article

Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques

Author(s): Penta Anil Kumar*, Ramalingam Gunasundari and Ramalingam Aarthi

Volume 17, Issue 8, 2021

Published on: 05 January, 2021

Page: [943 - 955] Pages: 13

DOI: 10.2174/1573405616666210105125542

Price: $65


Background: Magnetic Resonance Imaging (MRI) plays an important role in the field of medical diagnostic imaging as it poses non-invasive acquisition and high soft-tissue contrast. However, a huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterprets the data, and may cause discomfort to the patient. Thus, the main goal of MRI research is to accelerate data acquisition processing without affecting the quality of the image.

Introduction: This paper presents a survey based on distinct conventional MRI reconstruction methodologies. In addition, a novel MRI reconstruction strategy is proposed based on weighted Compressive Sensing (CS), Penalty-aided minimization function, and Meta-heuristic optimization technique.

Methods: An illustrative analysis is done concerning adapted methods, datasets used, execution tools, performance measures, and values of evaluation metrics. Moreover, the issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to obtain improved contribution for devising significant MRI reconstruction techniques.

Results: The proposed method will reduce conventional aliasing artifact problems, may attain lower Mean Square Error (MSE), higher Peak Signal-to-Noise Ratio (PSNR), and Structural SIMilarity (SSIM) index.

Conclusion: The issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to devising an improved significant MRI reconstruction technique.

Keywords: Magnetic resonance imaging, reconstruction, compressive sensing, penalty-aided minimization function, meta- heuristic optimization.

Graphical Abstract
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