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Semin Musculoskelet Radiol 2020; 24(01): 012-020
DOI: 10.1055/s-0039-3400265
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Improving the Speed of MRI with Artificial Intelligence

Patricia M. Johnson 
1   Center for Biomedical Imaging, NYU Langone Health, Radiology Department, New York, New York
,
Michael P. Recht
1   Center for Biomedical Imaging, NYU Langone Health, Radiology Department, New York, New York
,
Florian Knoll
1   Center for Biomedical Imaging, NYU Langone Health, Radiology Department, New York, New York
› Author Affiliations Funding Source We acknowledge grant support from the National Institutes of Health, grants NIH R01 EB024532 and NIH P41 EB017183. We also acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC); P. Johnson is the recipient of an NSERC Postdoctoral fellowship award.
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Publication History

Publication Date:
28 January 2020 (online)

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Abstract

Magnetic resonance imaging (MRI) is a leading image modality for the assessment of musculoskeletal (MSK) injuries and disorders. A significant drawback, however, is the lengthy data acquisition. This issue has motivated the development of methods to improve the speed of MRI. The field of artificial intelligence (AI) for accelerated MRI, although in its infancy, has seen tremendous progress over the past 3 years. Promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data. Preliminary studies show the promise of the variational network, a state-of-the-art technique, to generalize to many different anatomical regions and achieve comparable diagnostic accuracy as conventional methods. This article discusses the state-of-the-art methods, considerations for clinical applicability, followed by future perspectives for the field.

Keywords

magnetic resonance imaging - accelerated imaging - artificial intelligence - machine learning
 

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