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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