Abstract #0883

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

Rahel Heule¹, Jonas Bause¹, Orso Pusterla^2,3,4, and Klaus Scheffler^1,5

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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