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

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)Abstractpeer-review

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Original languageEnglish
Publication statusPublished - Aug 2020


TitleISMRM & SMRT Virtual Conference & Exhibition
Period8 - 14 August 2020


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.