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

Perlman, O; Zhu, B; Zaiss, M; Rosen, MS; Farrar, CT

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

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Zaiss, M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.ismrm.org/20/program_files/DP08-01.htm
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引用

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.

引用: https://hdl.handle.net/21.11116/0000-0006-D8B4-B

要旨

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.