  This chapter provides an overview of deep learning techniques for improving
the spatial resolution of MRI, ranging from convolutional neural networks,
generative adversarial networks, to more advanced models including
transformers, diffusion models, and implicit neural representations. Our
exploration extends beyond the methodologies to scrutinize the impact of
super-resolved images on clinical and neuroscientific assessments. We also
cover various practical topics such as network architectures, image evaluation
metrics, network loss functions, and training data specifics, including
downsampling methods for simulating low-resolution images and dataset
selection. Finally, we discuss existing challenges and potential future
directions regarding the feasibility and reliability of deep learning-based MRI
super-resolution, with the aim to facilitate its wider adoption to benefit
various clinical and neuroscientific applications.
