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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