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Abstract #3680

Ultra-low-dose Amyloid PET Reconstruction using Deep Learning with Multi-contrast MRI Inputs

Kevin T Chen1, Enhao Gong2, Fabiola Bezerra de Carvalho Macruz1, Junshen Xu3, Mehdi Khalighi4, John Pauly2, and Greg Zaharchuk1

1Radiology, Stanford University, Stanford, CA, United States, 2Electrical Engineering, Stanford University, Stanford, CA, United States, 3Engineering Physics, Tsinghua University, Beijing, China, 4GE Healthcare, Menlo Park, CA, United States

Simultaneous PET/MRI is a powerful hybrid imaging modality that allows for perfectly correlated morphological and functional information. With deep learning methods, we propose to use multiple MR images and a noisy, ultra-low-dose amyloid PET image to synthesize a diagnostic-quality PET image resembling that acquired with typical injected dose. This technique can potentially increase the utility of hybrid amyloid PET/MR imaging in clinical diagnoses and longitudinal studies.

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