Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets

Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quant...

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Bibliographic Details
Main Authors: Gang Xiao, Xiaolei Zhang, Hanjing Tang, Weipeng Huang, Yaowen Chen, Caiyu Zhuang, Beibei Chen, Lin Yang, Yue Chen, Gen Yan, Renhua Wu
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Neuroscience
Subjects:
chemical exchange saturation transfer
deep learning
dense Z-spectra reconstruction
sparse frequency offsets
sequence-to-sequence framework
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2023.1323131/full

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https://www.frontiersin.org/articles/10.3389/fnins.2023.1323131/full

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