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  MR-double-zero - Proof-of-concept for a framework to autonomously discover MRI contrasts

Glang, F., Mueller, S., Herz, K., Loktyushin, A., Scheffler, K., & Zaiss, M. (2022). MR-double-zero - Proof-of-concept for a framework to autonomously discover MRI contrasts. Journal of Magnetic Resonance, 341: 107237. doi:10.1016/j.jmr.2022.107237.

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 Creators:
Glang, F1, Author              
Mueller, S1, Author              
Herz, K1, Author              
Loktyushin, A1, Author              
Scheffler, K1, Author              
Zaiss, M1, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: Purpose: A framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed. Methods: A derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression. Results: It is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction. Conclusions: New MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by 're-discovery' of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design.

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 Dates: 2022-052022-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.jmr.2022.107237
 Degree: -

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Title: Journal of Magnetic Resonance
Source Genre: Journal
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Publ. Info: San Diego [etc.] : Academic Press
Pages: 10 Volume / Issue: 341 Sequence Number: 107237 Start / End Page: - Identifier: ISSN: 0022-2364
CoNE: https://pure.mpg.de/cone/journals/resource/954922651175_1