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Conference Paper

NN-driven mapping of multiple diffusion metrics at high to ultra-high resolution using the bSSFP frequency profile

MPS-Authors
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Birk,  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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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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Loktyushin,  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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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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Heule,  R
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://ismrm.ethz.ch/html/BookOfAbstracts_combined.pdf
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Citation

Birk, F., Glang, F., Loktyushin, A., Birkl, C., Ehses, P., Scheffler, K., et al. (2021). NN-driven mapping of multiple diffusion metrics at high to ultra-high resolution using the bSSFP frequency profile. In 23rd Annual Meeting of the German Chapter of the ISMRM (pp. S78-S81). Zürich, Switzerland: ETH Zürich.


Cite as: https://hdl.handle.net/21.11116/0000-0009-785D-9
Abstract
Asymmetries in the bSSFP fre-quency profile comprise rich information about microstructural tissue properties and white matter fiber orientation. A neural network-driven approach is presented to simultaneously map multiple diffusion metrics from phase-cy-cled bSSFP data acquired at 3T and 9.4T.