Attention-Based Multi-Offset Deep Learning Reconstruction for Accelerating Chemical Exchange Saturation Transfer MRI

We proposed an attention-based multi-offset network to exploit redundant anatomy information for the reconstruction of CEST-MR image (AMO-CEST). To the best of our knowledge, this is the first work using deep learning with varied radial sample patterns and multi-offset slices as input to accelerate CEST-MRI. Compared with other deep learning-based methods on the four times under-sampling mouse brain CEST dataset, the AMO-CEST achieved the best performance with an MMSE of, a PSNR of dB, and an SSIM. In conclusion, the proposed AMO-CEST network can accelerate the CEST-MRI at high down-sampling rate while maintaining good image quality.