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.

本章提供了关于深度学习技术用于改善 MRI 空间分辨率的概述，包括卷积神经网络、生成对抗网络，以及更高级的模型，如 transformers、扩散模型和隐式神经表示。我们探索了超分辨图像对临床和神经科学评估的影响，并涵盖网络架构、图像评估指标、网络损失函数和训练数据细节等各种实际主题，包括模拟低分辨率图像的下采样方法和数据集选择。最后，我们讨论了深度学习 MRI 超分辨率的现有挑战和潜在未来发展方向，旨在促进其广泛应用于各种临床和神经科学应用。