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
