Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians
Lin DJ, Johnson PM, Knoll F, Lui YW (2021)
Publication Type: Journal article, Review article
Publication year: 2021
Journal
Book Volume: 53
Pages Range: 1015-1028
Journal Issue: 4
DOI: 10.1002/jmri.27078
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.
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United States (USA) (US)How to cite
APA:
Lin, D.J., Johnson, P.M., Knoll, F., & Lui, Y.W. (2021). Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians. Journal of Magnetic Resonance Imaging, 53(4), 1015-1028. https://dx.doi.org/10.1002/jmri.27078
MLA:
Lin, Dana J., et al. "Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians." Journal of Magnetic Resonance Imaging 53.4 (2021): 1015-1028.
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