When generalizing a deep learning model to data acquired from different sites, non-harmonized protocols might result in missing or different data for use as model inputs. For an ultra-low-dose amyloid PET/MRI network we trained previously and wish to apply to other data, protocol differences resulted in missing MRI data and different PET image qualities. In this project we showed that structurally similar contrasts for substitution is a viable alternative in the case of missing input data and that noise reduction was observed when applying the network on any of the low-dose PET images.
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