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

Synopsis

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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