qMTNet+: artificial neural network with residual connection for accelerated quantitative magnetization transfer imaging

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Quantitative magnetization transfer (qMT) imaging provides quantitative measures of magnetization transfer properties, but the method itself suffers from long acquisition and processing time. Previous research has looked into the application of deep learning to accelerate qMT imaging. Specifically, a network called qMTNet was proposed to accelerate both data acquisition and fitting. In this study, we propose qMTNet+, an improved version of qMTNet, that accomplishes both acceleration tasks as well as generation of missing data with a single residual network. Results showed that qMTNet+ improves the quality of generated MT images and fitted qMT parameters compared to qMTNet.
Publisher
International Society for Magnetic Resonance in Medicine
Issue Date
2021-05-20
Language
English
Citation

2021 ISMRM & SMRT Annual Meeting & Exhibition, pp.2162

URI
http://hdl.handle.net/10203/286669
Appears in Collection
BiS-Conference Papers(학술회의논문)
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