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  In-Vivo Sub-Minute rNOE Mapping Using AutoCEST: a Machine-Learning Approach for CEST/MT Protocol Invention and Quantitative Reconstruction

Perlman, O., Zhu, B., Zaiss, M., Shono, N., Nakashima, H., Chiocca, E., et al. (2021). In-Vivo Sub-Minute rNOE Mapping Using AutoCEST: a Machine-Learning Approach for CEST/MT Protocol Invention and Quantitative Reconstruction. Poster presented at 2021 ISMRM & SMRT Annual Meeting & Exhibition (ISMRM 2021).

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Perlman, O, Author
Zhu, B, Author
Zaiss, M1, Author           
Shono, N, Author
Nakashima, H, Author
Chiocca, EA, Author
Rosen, MS, Author
Farrar, CT, Author
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: The long acquisition-time and the semi-quantitative nature of the typical CEST-MRI experiment constitute a major obstacle for its clinical adoption. Recently, a machine-learning approach termed AutoCEST was developed, for the automatic design of the optimal acquisition schedule and the reconstruction of quantitative 2-pool CEST maps. Here, we expand this approach for in-vivo scenarios, by incorporating the semisolid-pool into the underlying computational-graph and allowing 3 pools. AutoCEST was evaluated for quantitative rNOE mapping using a GBM mouse model, resulting in a total acquisition and reconstruction times of 49.15s. The tumor rNOE volume-fraction was significantly decreased, in agreement with previous human studies.

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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: 1455 Start / End Page: - Identifier: -