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Abstract #0142

Accelerate Parallel CEST Imaging with Dynamic Convolutional Recurrent Neural Network

Huajun She1, Quan Chen1, Shuo Li1, Kang Yan1, Xudong Chen1, Xi Chen1, Yuan Feng1, Jochen Keupp2, Robert Lenkinski3,4, Elena Vinogradov3,4, and Yiping P. Du1

1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Philips Research, Hamburg, Germany, 3Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States, 4Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States

CEST is a new contrast mechanism in MRI. However, a successful application of CEST is hampered by its slow acquisition. This work investigates accelerating parallel CEST imaging using dynamic convolutional recurrent neural networks. This work is the first try to apply recurrent neural networks to accelerate CEST imaging, which jointly learns the spatial and Z-spectral features. The in vivo brain results show that the proposed method demonstrates a much better reconstruction quality of the human brain MTRasym maps than the traditional dynamic compressed sensing method, while the reconstruction time is one hundred times shorter.

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