Brain MRI Image Super-Resolution Reconstruction: A Systematic Review

Magnetic Resonance Imaging (MRI) is pivotal in clinical diagnostics and neurological research, providing high-contrast, non-invasive imaging. However, the acquisition of high-resolution MRI is hampered by hardware limitations, extended scan durations, and low signal-to-noise ratios (SNR). To overcome these challenges, super-resolution (SR) techniques have emerged as a promising alternative, enhancing the resolution of low-resolution (LR) brain MRI images. This systematic review evaluates the current landscape of SR methods applied to brain MRI, analyzing recent studies’ algorithms, datasets, and evaluation metrics. We systematically retrieved and examined relevant literature from five major databases: Scopus, PubMed, IEEE Xplore, Google Scholar, and ACM Digital Library. Our analysis identifies key methodologies, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and Transformer-based models, significantly contributing to SR advancements. The review also highlights the challenges in accurately reconstructing high-resolution images, particularly in maintaining fine details at higher scaling factors. Evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are discussed to quantify the performance of these SR models. This comprehensive assessment provides insights into the current state of brain MRI SR, guiding future research and development efforts to improve diagnostic accuracy and reduce scan times.