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Journal Article

DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data – a proof of concept study

MPS-Authors
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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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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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Schuppert,  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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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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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/persons216054

Lindig,  T
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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https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.27690
(Publisher version)

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Citation

Zaiss, M., Deshmane, A., Schuppert, M., Herz, K., Glang, F., Ehses, P., et al. (2019). DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data – a proof of concept study. Magnetic Resonance in Medicine, 81(6), 3901-3914. doi:10.1002/mrm.27690.


Cite as: https://hdl.handle.net/21.11116/0000-0003-1295-0
Abstract
Purpose
To determine the feasibility of employing the prior knowledge of well‐separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z‐spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach trained with 3 T and 9.4 T CEST spectral data from brains of the same subjects.
Methods

Highly spectrally resolved Z‐spectra from the same volunteer were acquired by 3D‐snapshot CEST MRI at 3 T and 9.4 T at low saturation power of B1 = 0.6 µT. The volume‐registered 3 T Z‐spectra‐stack was then used as input data for a three layer deep neural network with the volume‐registered 9.4 T fitted parameter stack as target data.
Results

An optimized neural net architecture could be found and verified in healthy volunteers. The gray‐/white‐matter contrast of the different CEST effects was predicted with only small deviations (Pearson R = 0.89). The 9.4 T prediction was less noisy compared to the directly measured CEST maps, although at the cost of slightly lower tissue contrast. Application to an unseen tumor patient measured at 3 T and 9.4 T revealed that tumorous tissue Z‐spectra and corresponding hyper‐/hypointensities of different CEST effects can also be predicted (Pearson R = 0.84).
Conclusion

The 9.4 T CEST signals acquired at low saturation power can be accurately estimated from CEST imaging at 3 T using a neural network trained with coregistered 3 T and 9.4 T data of healthy subjects. The deepCEST approach generalizes to Z‐spectra of tumor areas and might indicate whether additional ultrahigh‐field (UHF) scans will be beneficial.