Keywords: CEST & MT, Machine Learning/Artificial IntelligenceHerein, we developed a partially separable network (PSN) for CEST acceleration. Our contributions are: 1) We found that the reconstruction error of CEST mainly exists in the spatial subspace. 2) A deep learning network based on partially separable model was developed to optimize CEST images in spatial subspace. Retrospective results suggested that our method enabled a highly accelerated CEST imaging (14X for healthy adults and 11X for brain tumor patients) with contrast maps and Z-spectrum consistent with gold standard, which could have great clinical utility.
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