CEST MRI is an unique molecular imaging approach to reveal the exchangeable proton information related to physiology and pathology. However, long scanning time has hindered its translation into clinics. While deep-learning based super-resolution methods have been explored to reduce scanning time in conventional MRI, adaptation of these methods to CEST MRI has been limited due to lack of large public CEST datasets. Therefore, this study proposes two transfer learning based super-resolution methods, Single-Offset UNet and Multi-Offset UNet, for accelerating CEST MRI acquisition by using public MRI databases for pretraining and a very small CEST dataset for training.
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