A robust multi-domain network for short-scanning amyloid PET reconstruction
This work addresses the challenge of improving image quality in amyloid PET scans with reduced scanning time, which is incremental as it builds on existing methods by enhancing multi-domain generalization.
The paper tackles the problem of restoring low-quality amyloid PET images from short scanning times by proposing a robust multi-domain network that uses mapping labels to learn representations across domains, achieving high accuracy in amyloid status classification (0.970 and 0.930 for two readers) and comparable or superior quantitative metrics in unseen domains.
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.