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Abstract #3123

Alzheimer’s Disease identification by an artificial neutral network based on chemical exchange saturation transfer (ANNCEST) MRI

Joseph H. C. Lai1, Jianpan Huang1, Xiongqi Han1, Jiadi Xu2,3, and Kannie W. Y. Chan1,2
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, 2Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States

There is an urgent need to develop an efficient and noninvasive methods to diagnose AD at an early stage. Artificial neural network (ANN) is a powerful model for prediction and classification of diseases, thus, it has been applied to facilitate prognosis and diagnosis. We propose to apply ANN based on chemical exchange saturation transfer (CEST) MRI at 3T to detect AD. Our phantom and AD mouse results showed that the trained ANN was able to identify AD from age-matched wild-type (WT) mice with high accuracy, which could provide valuable information for AD diagnosis.

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