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Abstract #2638

Mapping metabolic activation as FDG-PET/Amyloid-PET using Contrast-free MRI and Deep Learning

Enhao Gong1, Kevin Chen2, Jia Guo2, Audrey Fan2, John Pauly1, and Greg Zaharchuk2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

MRI has great clinical values to distinguish soft-tissues without contrast or radiation. By using the hybrid-modality information from MRI and PET, here we developed deep learning method to synthesize metabolic activity mapping from contrast-free multi-contrast MRI images. Trained on clinical datasets, we demonstrated the feasibility to estimate metabolic biomarker from contrast-free MRI and validated on both FDG-PET/MRI and Amyloid-PET/MRI in-vivo datasets. This technique can be used for more efficient, low-cost, multi-tracer functional imaging, exploring anatomy-function relationship, visualizing new bio-markers and improving the workflow for both MRI and PET/MRI.

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