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.
Calibrationless MRI Reconstruction With A Plug-In Denoiser
