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Abstract #2194

Generalizing Ultra-low-dose PET/MRI Networks Across Radiotracers: From Amyloid to Tau

Kevin T. Chen1, Olalekan Adeyeri2, Tyler N Toueg3, Elizabeth Mormino3, Mehdi Khalighi1, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2Salem State University, Salem, MA, United States, 3Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States

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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