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Abstract #0883

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

Rahel Heule1, Jonas Bause1, Orso Pusterla2,3,4, and Klaus Scheffler1,5
1High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland, 3Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland, 4Department of Biomedical Engineering, University of Basel, Basel, Switzerland, 5Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany

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.

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