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

Weinmüller, S; Dang, HN; Loktyushin, A; Glang, F; Doerfler, A; Maier, A; Schölkopf, B; Scheffler, K; Zaiss, M

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

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Loktyushin, A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons230667

Glang, F
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf, B
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84187

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;

/persons/resource/persons214560

Zaiss, M
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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https://www.ismrm.org/21/program-files/TeaserSlides/TeasersPresentations/1761-Teaser.html
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Citation

Weinmüller, S., Dang, H., Loktyushin, A., Glang, F., Doerfler, A., Maier, A., et al. (2021). MRzero sequence generation using analytic signal equations as forward model and neural network reconstruction for efficient auto-encoding. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).

Cite as: https://hdl.handle.net/21.11116/0000-0008-8693-A

Abstract

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.