Attention-Based Multi-Offset Deep Learning Reconstruction for Accelerating Chemical Exchange Saturation Transfer MRI
Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages | 0836 |
Publication status | Published - May 2023 |
Meeting
Title | 2023 ISMRM & ISMRT Annual Meeting & Exhibition |
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Location | Metro Toronto Convention Centre (MTCC) |
Place | Canada |
City | Toronto, ON |
Period | 3 - 8 June 2023 |
Link(s)
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(ce7e2c8b-9c5d-4b52-928d-181c9d794a02).html |
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Abstract
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.
Research Area(s)
- Image Reconstruction, Artificial Intelligence (AI), Machine Learning/Artificial Intelligence
Citation Format(s)
Attention-Based Multi-Offset Deep Learning Reconstruction for Accelerating Chemical Exchange Saturation Transfer MRI. / Yang, Zhikai; Liu, Yang; Pemmasani Prabakaran, Rohith Saai et al.
2023. 0836 Paper presented at 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, ON, Canada.
2023. 0836 Paper presented at 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, ON, Canada.
Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review