Abstract #1757

Accelerating Chemical Exchange Saturation TransferImaging using a Diffusion Model

Yue Wang^1,2,3, Xi Xu¹, Zhuo-Xu cui¹, Haifeng Wang¹, Yihang Zhou¹, Dong Liang¹, Hairong Zheng¹, and Yanjie Zhu¹

¹Shenzhen Institute of Advanced Technology，Chinese Academy of Sciences, Shenzhen, China, ²Medical AI Lab，School of Biomedical Engineering, Shenzhen University Medical School,, Shenzhen University, Shenzhen, China, ³Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging，School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China

Synopsis

Keywords: AI/ML Image Reconstruction, CEST & MT

Motivation: Chemical exchange saturation transfer (CEST) magnetic resonance (MR) imaging is slow, and rapid radial undersampling significantly compromises the image quality of CEST data.

Goal(s): Our goal is to enhance the image performance of CEST reconstruction under higher radial undersampling.

Approach: A diffusion model is introduced to obtain prior information from MRI data, and its performance is evaluated on CEST data under radial sampling.

Results: The proposed method generated high-quality CEST source images in healthy human data, outperforming iGRASP.

Impact: The proposed method has achieved rapid imaging of CEST data, providing high-quality CEST source images .

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