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pH mapping of brain tissue by a deep neural network trained on 9.4T CEST MRI data: pH-deepCEST

MPS-Authors
/persons/resource/persons238878

Mueller,  S
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons230667

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;

/persons/resource/persons84187

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;

/persons/resource/persons214560

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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https://www.ismrm.org/20/program_files/DP08-01.htm
(Abstract)

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Citation

Mueller, S., Glang, F., Scheffler, K., & Zaiss, M. (2020). pH mapping of brain tissue by a deep neural network trained on 9.4T CEST MRI data: pH-deepCEST. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.


Cite as: https://hdl.handle.net/21.11116/0000-0006-D8B6-9
Abstract
The pH value is of major importance for most physiological processes and may change due to altered metabolism in pathologies. In the present work, we exploit the inherent dependency of CEST MR data on pH with a new approach: train neural networks to map voxel-by-voxel from multi-B1+ CEST spectra to pH value. Measurements were performed in homogenate of pig brain tissue at 9.4T ultra high field. Prediction of absolute pH values was possible and predictions were stable against inhomogeneity in B1+. We hope this proof of concept might be a first small step towards high-resolution 3D pH maps in vivo.