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  AutoCEST: a Machine-Learning Approach for Optimal CEST-MRI Experiment Design and Quantitative Mapping

Perlman, O., Zhu, B., Zaiss, M., Rosen, M., & Farrar, C. (2020). AutoCEST: a Machine-Learning Approach for Optimal CEST-MRI Experiment Design and Quantitative Mapping. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.

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Perlman, O, Author
Zhu, B, Author
Zaiss, M1, 2, Author              
Rosen, MS, Author
Farrar, CT, 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              

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 Abstract: The most common metric for CEST analysis is the magnetization-transfer-ratio asymmetry. Although qualitatively useful, it is affected by a mixed contribution from several exchange properties and requires experiment-specific protocol optimization. Herein, we propose a machine-learning framework for simultaneously tackling two challenging tasks: (1) automatic design of the optimal CEST acquisition schedule; (2) automatic extraction of fully quantitative CEST maps from the acquired data. The method was evaluated in simulations and phantoms at 4.7T. The resulting data acquisition and reconstruction times were 52 s and 36 ms respectively, providing quantitative exchange-rate and volume fraction maps with good agreement to ground-truth.

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 Dates: 2020-08
 Publication Status: Published online
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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
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Start-/End Date: 2020-08-08 - 2020-08-14

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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 3098 Start / End Page: - Identifier: -