Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date:

Abstract

Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.

Authors

  • Dana J Lin
    Division of Musculoskeletal Radiology, Department of Radiology, NYU Langone Health, New York, New York.
  • Patricia M Johnson
    Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.
  • Florian Knoll
    Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA.
  • Yvonne W Lui
    Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA.