Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceWe 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.
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