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キーワード:
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要旨:
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.
