Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

• Published in: Journal of magnetic resonance imaging Vol. 53; no. 4; pp. 1015 - 1028
• Main Authors: Lin, Dana J., Johnson, Patricia M., Knoll, Florian, Lui, Yvonne W.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2021; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial Intelligence; Computational neuroscience; Data acquisition; Deep learning; Humans; Image acquisition; Image classification; Image processing; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Image segmentation; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical imaging; MRI; Neuroimaging; Radiography; Therapeutic applications; deep learning; MRI; image reconstruction
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27078

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.