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Abstract #2422

Does Simultaneous Morphological Inputs Matter for Deep Learning Enhancement of Ultra-low Amyloid PET/MRI?

Kevin T. Chen1, Olalekan Adeyeri2, Tyler N Toueg3, Elizabeth Mormino3, Mehdi Khalighi1, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2Salem State University, Salem, MA, United States, 3Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States

We have previously generated diagnostic quality amyloid positron emission tomography (PET) images with deep learning enhancement of actual ultra-low-dose (~2% of the original) PET images and simultaneously acquired structural magnetic resonance imaging (MRI) inputs. Here, we will investigate whether simultaneity is a requirement for such structural MRI inputs. If simultaneity is not required, this will increase the utility of MRI-assisted ultra-low-dose PET imaging by including data acquired on separate PET/ computed tomography (CT) and standalone MRI machines.

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