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  MRzero: Automated invention of MRI sequences using supervised learning

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).

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 Creators:
Loktyushin, A1, 2, Author              
Herz, K1, 2, Author              
Dang, N, Author
Glang, F1, 2, Author              
Deshmane, A1, 2, Author              
Weinmüller, F, 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: 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.

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 Dates: 2021-05
 Publication Status: Published online
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Title: 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021)
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Start-/End Date: 2021-05-15 - 2021-05-20

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Title: 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 4199 Start / End Page: - Identifier: -