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

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.

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Birk, F1, Author              
Steiglechner, J1, Author              
Scheffler, K1, Author              
Heule, R1, Author              
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 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.

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 Dates: 2022-05
 Publication Status: Published online
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Title: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022)
Place of Event: London, UK
Start-/End Date: 2022-05-07 - 2022-05-12

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Title: Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM 2022)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 2532 Start / End Page: - Identifier: -