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Semin Musculoskelet Radiol 2020; 24(04): 451-459
DOI: 10.1055/s-0040-1709482
Review Article

Improving Quantitative Magnetic Resonance Imaging Using Deep Learning

Fang Liu 
1   Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Abstract

Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

Keywords

deep learning - magnetic resonance imaging - quantitative imaging - rapid imaging - relaxometry

Financial Disclosure

Fang Liu receives funding support from National Institutes of Health grants NIH P41-EB022544 and R01-CA165221.




Publication History

Article published online:
29 September 2020

© 2020. Thieme. All rights reserved.

Thieme Medical Publishers
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