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MRzero: Automated invention of MRI sequences using supervised learning

MPS-Authors
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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/persons216025

Herz,  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/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/persons215996

Deshmane,  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/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;

External Resource

https://www.ismrm.org/21/program-files/TeaserSlides/TeasersPresentations/4199-Teaser.html
(Abstract)

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Citation

Loktyushin, A., Herz, K., Dang, N., Glang, F., Deshmane, A., Weinmüller, F., et al. (2021). MRzero: Automated invention of MRI sequences using supervised learning. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).


Cite as: https://hdl.handle.net/21.11116/0000-0008-865B-B
Abstract
We propose a framework — MRzero — that allows automatic invention of MR sequences. At the core of the framework is a differentiable forward process allowing to simulate image measurement and reconstruction. The sequence parameters are variables of optimization. As a cost function we use mean squared error distance to a certain given target contrast of interest. To avoid overfitting we propose a method that generates synthetic data that is used for training. In the experiments, we demonstrate the ability of the method to learn RF flip angles and spatial encoding from scratch given a target obtained with GRE sequence.