An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST)

An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST)

Purpose: To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. Methods: An MR physics-governed AI system was trained to generate optimized acquisition...

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Bibliographic Details
Main Authors: Farrar, C.T (Author), Perlman, O. (Author), Rosen, M.S (Author), Zaiss, M. (Author), Zhu, B. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2022
Subjects:
Acquisition protocols
Automated discovery
Chemical exchange saturation transfer
chemical exchange saturation transfer (CEST)
deep learning
End to end
Errors
Image reconstruction
In-vivo
Magnetic resonance
magnetic resonance fingerprinting (MRF)
magnetization transfer (MT)
Mean absolute error
Mouse brain
optimization
Phantoms
Protocol parameters
quantitative imaging
Saturation power
Spin dynamics
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