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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, Autor           
Herz, K1, 2, Autor           
Dang, N, Autor
Glang, F1, 2, Autor           
Deshmane, A1, 2, Autor           
Weinmüller, S, Autor
Doerfler, A, Autor
Schölkopf, B3, Autor           
Scheffler, K1, 2, Autor           
Zaiss, M1, 2, Autor           
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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 Zusammenfassung: 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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 Datum: 2021-032021-08
 Publikationsstatus: Erschienen
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 Identifikatoren: DOI: 10.1002/mrm.28727
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Titel: Magnetic Resonance in Medicine
Genre der Quelle: Zeitschrift
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Affiliations:
Ort, Verlag, Ausgabe: New York : Wiley-Liss
Seiten: - Band / Heft: 86 (2) Artikelnummer: - Start- / Endseite: 709 - 724 Identifikator: ISSN: 0740-3194
CoNE: https://pure.mpg.de/cone/journals/resource/954925538149