We have previously trained a deep learning network with simultaneous PET/MRI inputs to generate diagnostic quality images from ultra-low-dose amyloid PET acquisitions. With data bias being a known issue in deep learning-based applications, we aim to investigate whether this network could generalize to ultra-low-dose tau PET image enhancement. Results of this study show that data bias across radiotracers needs to be accounted for before applying an ultra-low-dose network trained on one tracer to another.
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