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Journal Article

MRzero: Automated discovery 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;

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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;

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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;

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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;

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

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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;

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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://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.28727
(Publisher version)

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Citation

Loktyushin, A., Herz, K., Dang, N., Glang, F., Deshmane, A., Weinmüller, S., et al. (2021). MRzero: Automated discovery of MRI sequences using supervised learning. Magnetic Resonance in Medicine, 86(2), 709-724. doi:10.1002/mrm.28727.


Cite as: https://hdl.handle.net/21.11116/0000-0008-332B-F
Abstract
Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies.

Methods: The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T1
and T2 , and ΔB0 . As a proof of concept, we use both conventional MR images and T1

maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time.

Results: In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo.

Conclusions: Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.