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Abstract #3847

Single-offset and multi-offset super-resolution for CEST MRI using deep transfer learning

Rohith Saai Pemmasani Prabakaran1, Zilin Chen2, Joseph H.C. Lai2, Se Weon Park1,2, Yang Liu1,2, Jianpan Huang2, and Kannie W.Y. Chan1,2,3,4
1Hong Kong Centre For Cerebro-Cardiovascular Health Engineering, Hong Kong, Hong Kong, 2Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, 3Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 4City University of Hong Kong Shenzhen Research Institute, Shenzhen, China


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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