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1 results

  • 9 - k-Space Deep Learning for MR Reconstruction and Artifact Removal

  • from Part II - Deep-Learning Architecture for Various Imaging Architectures
  • Edited by Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST), Yonina C. Eldar, Weizmann Institute of Science, Israel, Michael Unser, École Polytechnique Fédérale de Lausanne
  • Book:
    Deep Learning for Biomedical Image Reconstruction
    Published online:
    15 September 2023
    Print publication:
    12 October 2023, pp 200-222

  • Summary

    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.