A Robust Multidomain Network for Short-Scanning Amyloid PET Image Restoration

Abstract

This study presents a deep-learning-based restoration method for low-quality amyloid positron emission tomography (PET) images acquired in a short period, which can be generalized across multiple domains. Each of these domains consists of low-quality amyloid PET images acquired in the same environment. Owing to variations in image characteristics, such as contrast, across different acquisition environments, the restoration performance of the deep-learning methods can significantly degrade when applied to PET images obtained from unseen domains (i.e., not seen in training). To address the difficulty, we introduce a mapping label and condition the network on this label. This enables the network that takes a low-quality amyloid PET image and the corresponding mapping label as inputs to effectively generate the desired high-quality amyloid PET image. We assign the mapping label as a one-hot vector for each domain and use pairs of PET images from short (2 min) and standard (20 min) scanning times for training. The network, trained with the mapping label, can efficiently restore low-quality amyloid PET images in unseen domains by estimating an unknown mapping label for the unseen domain. We demonstrate the effectiveness of the proposed method through quantitative and qualitative analyses on the several datasets.