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 Lenkinski^3,4, Elena Vinogradov^3,4, 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 MTR_asym maps than the traditional dynamic compressed sensing method, while the reconstruction time is one hundred times shorter.

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