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Abstract #1128

Transfer Learning of an Ultra-low-dose Amyloid PET/MRI U-Net Across Scanner Models

Kevin T Chen1, Matti Schürer2, Jiahong Ouyang1, Enhao Gong1, Solveig Tiepolt2, Osama Sabri2, Greg Zaharchuk1, and Henryk Barthel2

1Stanford University, Stanford, CA, United States, 2University of Leipzig, Leipzig, Germany

Reducing the radiotracer dose of amyloid PET/MRI will increase utility of this modality for early diagnosis and for multi-center trials on amyloid-clearing drugs. To do so networks trained on data from one site will have to be applied to data acquired on other sites. In this project we have shown that after fine-tuning, a network trained on PET/MRI data acquired from one scanner is able to produce diagnostic-quality images while achieving noise reduction and image quality improvement for data acquired on another scanner.

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