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Abstract #2848

MRI-assisted Deep Learning-enhanced Actual Ultra-low-dose Amyloid PET Acquisitions  

Kevin T Chen1, Dawn Holley1, Kim Halbert1, Tyler N Toueg1, Athanasia Boumis1, Elizabeth Mormino1, Mehdi Khalighi1, and Greg Zaharchuk1
1Stanford University, Stanford, CA, United States

We are aiming to greatly reduce the radioactive radiotracer dose administered to subjects during PET scanning. In this work we propose to leverage the perfect spatiotemporal correlation of hybrid PET/MRI scanning to synthesize diagnostic PET images from multiple MR images and a noisy PET image reconstructed from acquisitions with actual ultra-low-dose (as low as ~1% of the original) amyloid radiotracer injections, using trained deep neural networks. This technique can potentially increase the utility of hybrid amyloid PET/MR imaging and remove the limiting factors to large-scale clinical longitudinal PET/MRI studies.

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