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Abstract #2716

FAR-CEST: Fast Acquisition and Reconstruction for Chemical Exchange Saturation Transfer (CEST) imaging using a Deep-Learning Approach

Chuyu Liu1, Zhensen Chen2, Yibing Chen3, Xubin Chai1, Zhongsen Li1, and Xiaolei Song1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, 2Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 3Xi’an Key Lab of Radiomics and Intelligent Perception, School of Information Sciences and Technology, Northwest University, Xi'an, China


We developed a Fast Acquisition and Reconstruction CEST (FAR-CEST) method at 3T human scanners, based on a deep learning approach. A 10X accelerated acquisition was achieved, which under-sampled K-space using a randomized Cartesian pattern of variable density. To fully utilize the correlation among saturation offset dimension, especially to compensate for sparsely-sampled K-space edge, a 3D-Res-Unet model was trained for reconstruction. Results on healthy adult brain suggested that FAR-CEST can produce high quality saturation-weighted images and Z-spectra,but the CEST contrast slightly altered. The highly-acceleration feature of FAR-CEST has been initially validated, yet still require improvement on reconstruction accuracy.

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