Abstract #3707

CEST and AREX data processing based on deep neural network: application to image Alzheimer’s disease at 3T

Jianpan Huang¹, Joseph H. C. Lai¹, Kai-Hei Tse², Gerald W.Y. Cheng², Xiongqi Han¹, Yang Liu¹, Zilin Chen¹, Lin Chen^3,4, Jiadi Xu^3,4, and Kannie W. Y. Chan^1,4,5

¹Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China, ²Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China, ³F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States, ⁴Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵City University of Hong Kong Shenzhen Research Institute, Shenzhen, China

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a promising molecular imaging technology. Apparent exchange-dependent relaxation (AREX) provides CEST contrast with less influence of T₁. Here, deep neural network based CEST/AREX analysis methods (CESTNet/AREXNet) were applied to analyze the CEST data of normal and AD mouse brains at 3T. Significant lower amide proton transfer/magnetization transfer (APT/MT) signals related to amyloid β-peptide (Aβ) plaque depositions, which were validated by immunohistochemistry results, were detected in Alzheimer’s disease (AD) mouse brains compared to age-matched wild type (WT) mouse brains. The well-established CESTNet/AREXNet have great potential to facilitate AD identification at 3T.

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