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Poster

Advances in MRzero: supervised learning of parallel imaging sequences including joint non-Cartesian trajectory and flip angle optimization

MPG-Autoren
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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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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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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;

/persons/resource/persons84193

Schölkopf,  B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, 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;

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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;

Externe Ressourcen

https://www.ismrm.org/21/program-files/D-105.htm
(Zusammenfassung)

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Zitation

Glang, F., Loktyushin, A., Herz, K., Dang, H., Deshmane, A., Weinmüller, S., et al. (2021). Advances in MRzero: supervised learning of parallel imaging sequences including joint non-Cartesian trajectory and flip angle optimization. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).


Zitierlink: https://hdl.handle.net/21.11116/0000-0008-866E-6
Zusammenfassung
Recently, MRzero has been proposed as a fully differentiable Bloch-equation-based MRI sequence invention framework. In this work, the approach is extended by parallel imaging capability, employing a CG SENSE reconstruction that allows optimizing for non-Cartesian sampling trajectories simultaneously with other sequence parameters like RF pulses and timings. The approach is tested herein by simulations on an in silico brain phantom and is found to yield improved reconstructions compared to regular Cartesian undersampling, and to simultaneously find variable flip angle patterns that compensate for transient signal induced blurring