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

Research output: Conference PapersRGC 32 - Refereed conference paper (without host publication)peer-review

View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages0836
Publication statusPublished - May 2023

Meeting

Title2023 ISMRM & ISMRT Annual Meeting & Exhibition
LocationMetro Toronto Convention Centre (MTCC)
PlaceCanada
CityToronto, ON
Period3 - 8 June 2023

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.

Research output: Conference PapersRGC 32 - Refereed conference paper (without host publication)peer-review