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Magnetic Resonance Imaging (MRI) is a critical imaging technique that provides detailed visualization of internal structures without harmful radiation. This review focuses on key MRI modalities, including T1-weighted and T2-weighted imaging, functional MRI (fMRI), and diffusion MRI (dMRI). T1-weighted images offer precise anatomical details, whereas T2-weighted images are essential for highlighting abnormalities such as tumors and inflammation. Functional MRI (fMRI) captures blood flow changes related to neural activity, and diffusion MRI (dMRI) tracks the movement of water molecules within brain tissues. Our review synthesizes insights from 173 studies across major databases, including PubMed, ACM Digital Library, IEEE Xplore, and Google Scholar. We emphasize the versatility of transformer architectures in neuroimaging applications, such as segmentation, detection, reconstruction, and super-resolution, with a particular focus on brain tumor segmentation as a notable achievement. Despite these successes, there remains a significant gap in research, highlighting the need for further studies and collaborative efforts to fully realize the potential of transformers in brain MRI applications. Following the PRISMA-ScR guidelines, this analysis explores current trends, dataset availability, and the overall research landscape. It calls on the scientific community to investigate the underexplored capabilities of transformers, aiming to inspire comprehensive research that could revolutionize brain MRI analysis and advance the fields of medical imaging and neuroscience.
Hayat et al. (Mon,) studied this question.