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Deep-Learning Driven Acceleration of Multi-Parametric Quantitative Phase-Cycled bSSFP Imaging

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Heule,  R
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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Bause,  J
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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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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Citation

Heule, R., Bause, J., Pusterla, O., & Scheffler, K. (2020). Deep-Learning Driven Acceleration of Multi-Parametric Quantitative Phase-Cycled bSSFP Imaging. In 2020 ISMRM & SMRT Virtual Conference & Exhibition (pp. 303).


Cite as: http://hdl.handle.net/21.11116/0000-0006-D7B9-7
Abstract
Prominent asymmetries in the bSSFP frequency profile in tissues with distinct fiber pathways are known to be a confounding factor in the quantification of relaxation times from a series of phase-cycled scans. It has been demonstrated that the resulting bias can be eliminated by training artificial neural networks using gold standard relaxation times as target. Here, the ability of neural networks to not only provide gold standard brain tissue T1 and T2 values as well as field map estimates (B1, ∆B0) but also to highly accelerate the acquisition by reducing the number of phase-cycles is explored.