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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 Chen3,4, Jiadi Xu3,4, and Kannie W. Y. Chan1,4,5
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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