Inspired by the success of deep learning in computer vision tasks, deep learning approaches for various MRI problems have been extensively studied in recent years. Early deep learning studies for MRI reconstruction and enhancement were mostly based on image-domain learning. However, because the MR signal is acquired in the k-space domain, researchers have demonstrated that deep neural networks can be directly designed in k-space to utilize the physics of MR acquisition. In this chapter, the recent trend of k-space deep learning for MRI reconstruction and artifact removal are reviewed. First, scan-specific k-space learning, which is inspired by parallel MRI, is covered. Then we provide an overview of data-driven k-space learning. Subsequently, unsupervised learning for MRI reconstruction and motion artifact removal are discussed.

The development of deep learning reconstruction methods for accelerated MR acquisitions has been an ongoing area of research for the last several years. It has been repeatedly demonstrated that deep learning methods can outperform classic reconstruction approaches in terms of both quantitative image metrics like MSE to ground truth as well as qualitative reader studies where radiologists have been questioned in a subjective way. We present the basics and well-known approaches for MR image reconstruction via deep learning.