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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