Yu-Wei Chang, Giovanni Volpe, Joana B Pereira
Date: 22 August 2023
Time: 10:15 AM PDT
In vivo tau-positron emission tomography (PET) is crucial for determining the stage of Alzheimer’s disease (AD). However, this method is expensive, not widely available, and exposes patients to ionizing radiation, which poses a carcinogenic risk. To address this issue, I’ll present our proposed method, a deep-learning synthesis approach for follow-up tau-PET brain images from baseline tau-PET images using a generative adversarial network (GAN). This technique has the potential to provide valuable insights into the progression of AD, the effectiveness of new treatments, and more accurate diagnosis of the disease.