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Abstract #0836

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

Zhikai Yang1,2, Liu Yang1,2, Rohith Saai Pemmasani Prabakaran2, AbdulMojeed Olabisi ILYAS2, Jianpan Huang1, and Kannie W. Y. Chan1,2,3,4,5
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, 2Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, Hong Kong, 3City University of Hong Kong Shenzhen Research Institute, Shenzhen, China, 4Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 5Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, Hong Kong

Synopsis

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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