  This paper presents a robust multi-domain network designed to restore
low-quality amyloid PET images acquired in a short period of time. The proposed
method is trained on pairs of PET images from short (2 minutes) and standard
(20 minutes) scanning times, sourced from multiple domains. Learning relevant
image features between these domains with a single network is challenging. Our
key contribution is the introduction of a mapping label, which enables
effective learning of specific representations between different domains. The
network, trained with various mapping labels, can efficiently correct amyloid
PET datasets in multiple training domains and unseen domains, such as those
obtained with new radiotracers, acquisition protocols, or PET scanners.
Internal, temporal, and external validations demonstrate the effectiveness of
the proposed method. Notably, for external validation datasets from unseen
domains, the proposed method achieved comparable or superior results relative
to methods trained with these datasets, in terms of quantitative metrics such
as normalized root mean-square error and structure similarity index measure.
Two nuclear medicine physicians evaluated the amyloid status as positive or
negative for the external validation datasets, with accuracies of 0.970 and
0.930 for readers 1 and 2, respectively.
