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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