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Investigating complex-valued neural networks applied to phase-cycled bSSFP for multi-parametric quantitative tissue characterization

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

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Steiglechner,  J
Department High-Field Magnetic Resonance, 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;

/persons/resource/persons216029

Heule,  R
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://submissions.mirasmart.com/ISMRM2022/Itinerary/ConferenceMatrixEventDetail.aspx?ses=D-33
(Abstract)

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Citation

Birk, F., Steiglechner, J., Scheffler, K., & Heule, R. (2022). Investigating complex-valued neural networks applied to phase-cycled bSSFP for multi-parametric quantitative tissue characterization. Poster presented at Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022), London, UK.


Cite as: https://hdl.handle.net/21.11116/0000-000A-5C2F-C
Abstract
The bSSFP sequence is highly sensitive to relaxation parameters, tissue microstructure, and off-resonance frequencies, which has recently been shown to enable multi-parametric tissue characterization in the human brain using real-valued NNs. In this work, a new approach based on complex-valued NNs for voxel-wise simultaneous multi-parametric quantitative mapping with phase-cycled bSSFP input data is presented, possibly facilitating data handling. Relaxometry parameters (T1, T2) and field map estimates (B1+, ΔB0) could be quantified with high robustness and accuracy. The quantitative results were compared for different activation functions, favoring phase-sensitive implementations.