With the focal and lower uptake in tau PET imaging compared to other tracers such as amyloid, we aim to use multimodal simultaneous PET/MR imaging combined with training a generative adversarial network (GAN) to enhance ultra-low-dose 18F-PI-2620 tau PET images. We showed that the deep learning-enhanced images greatly reduced image noise as compared to the ultra-low-dose images, outperformed the ultra-low-dose images metrics-wise, and were able to be read clinically for regional uptake patterns of tau accumulation similarly as the full-dose images.
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