Penta Anil Kumar*, R. Gunasundari and R. Aarthi
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, the huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterpretation of data, and may cause uncomfortable 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 artifacts 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.
Magnetic Resonance Imaging, Reconstruction, Compressive sensing, Penalty-aided minimization function, Metaheuristic optimization
Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, SRM Easwari Engineering College, Ramavaram