A Comprehensive Review of Techniques and Use Cases of Deep Learning for MRI Neuroimaging

The paper reviews the integration of deep learning techniques in magnetic resonance neuroimaging (MRI) to enhance analysis and diagnosis in brain imaging. MRI, a powerful tool for noninvasive neuroimaging, provides insights into brain structure and function through modalities like structural MRI (sMRI), diffusion MRI (dMRI), and functional MRI (fMRI). Deep learning, especially convolutional neural networks (CNN), has advanced applications in neuroimaging by overcoming limitations in traditional machine learning, which relied on manual feature extraction. In sMRI, CNNs support tasks like image segmentation and artifact correction, while generative adversarial networks (GANs) enhance image synthesis. In dMRI, CNNs aid in tractography and connectivity mapping, which is essential for examining structural brain connections. Meanwhile, recurrent neural networks (RNNs) and autoencoders (AEs) have been effective in fMRI for functional connectivity and brain state decoding, including cognitive task analysis. The paper discusses challenges like the need for harmonized data across imaging sites and optimizing models for small datasets, suggesting future potential in transfer learning and hybrid model applications for more robust, generalizable insights. This comprehensive review highlights the promise of deep learning in refining neuroimaging methods, supporting disease prediction, and improving diagnostic accuracy in conditions such as Alzheimer’s, schizophrenia, and traumatic brain injury.