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  MRzero: Automated discovery of MRI sequences using supervised learning

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.

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Loktyushin, A1, 2, Author              
Herz, K1, 2, Author              
Dang, N, Author
Glang, F1, 2, Author              
Deshmane, A1, 2, Author              
Weinmüller, S, Author
Doerfler, A, Author
Schölkopf, B3, Author              
Scheffler, K1, 2, Author              
Zaiss, M1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 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.

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 Dates: 2021-032021-08
 Publication Status: Published in print
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 Identifiers: DOI: 10.1002/mrm.28727
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Title: Magnetic Resonance in Medicine
Source Genre: Journal
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Publ. Info: New York : Wiley-Liss
Pages: - Volume / Issue: 86 (2) Sequence Number: - Start / End Page: 709 - 724 Identifier: ISSN: 0740-3194
CoNE: https://pure.mpg.de/cone/journals/resource/954925538149