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, EA; Rosen, MS; Farrar, CT

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

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

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https://www.ismrm.org/21/program-files/D-42.htm
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Citation

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).

Cite as: https://hdl.handle.net/21.11116/0000-0008-877C-5

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.