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Can a neural network predict B0 maps from uncorrected CEST-spectra?

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Glang,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Deshmane,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Martin,  F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Herz,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zaiss,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Glang, F., Deshmane, A., Martin, F., Herz, K., Scheffler, K., & Zaiss, M. (2019). Can a neural network predict B0 maps from uncorrected CEST-spectra?. Poster presented at 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019), Montréal, QC, Canada.


Cite as: http://hdl.handle.net/21.11116/0000-0003-96D0-8
Abstract
Analysis of chemical exchange saturation transfer (CEST) effects suffers from B0 inhomogeneity. Common correction methods involve computationally expensive algorithms or even additional measurements. Here we demonstrate that deep neural networks are able to predict B0 maps from raw Z-spectra by training the networks with measured B0 maps. Moreover, we show that CEST contrast parameters representing amide, amine and NOE resonance peaks can be directly predicted from uncorrected Z-spectra in a fast single step. This provides a shortcut to conventional evaluation procedures and will be useful to guide nonlinear model fitting.