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Abstract #2532

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

Florian Birk1, Julius Steiglechner1, Klaus Scheffler1,2, and Rahel Heule1,2
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany


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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