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Abstract #1761

MRzero sequence generation using analytic signal equations as forward model and neural network reconstruction for efficient auto-encoding

Simon Weinmüller1, Hoai Nam Dang1, Alexander Loktyushin2,3, Felix Glang2, Arnd Doerfler1, Andreas Maier4, Bernhard Schölkopf3, Klaus Scheffler2,5, and Moritz Zaiss1,2
1Neuroradiology, University Clinic Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 2Max-Planck Institute for Biological Cybernetics, Magnetic Resonance Center, Tübingen, Germany, 3Max-Planck Institute for Intelligent Systems, Empirical Inference, Tübingen, Germany, 4Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany, 5Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany

MRzero is a fully differentiable Bloch-equation-based MRI sequence invention framework. Instead of using time-consuming average-isochromat-based Bloch simulations, analytic signal equations are used as alternative forward differentiable MR scan simulation method. Neural network reconstruction is used for efficient auto-encoding. The joint optimization of sequence and NN parameters for B1 and T1 mapping can be performed 2 to 3 orders of magnitude faster then in previous MRzero approaches. The optimized sequence is tested by measurements in vivo at 3T and compared to a standard inversion recovery. High quality B1 and T1 maps are provided with less total acquisition time and energy deposition.

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