AI Approach of Cycle-Consistent Generative Adversarial Networks to Synthesize PET Images to Train Computer-Aided Diagnosis Algorithm for Dementia.

OBJECTIVE:An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are available for dementia with Lewy bodies and frontotemporal lobar degeneration. Therefore, this study aims to present the potential of cycle-consistent generative adversarial networks (CycleGAN) to obtain enough number of training images for AI-based computer-aided diagnosis (CAD) algorithms for diagnosing dementia.METHODS:We trained CycleGAN using 43 amyloid-negative and 45 positive images in slice-by-slice.RESULTS:The CycleGAN can be used to synthesize reasonable amyloid-positive images, and the continuity of slices was preserved.DISCUSSION:Our results show that CycleGAN has the potential to generate a sufficient number of training images for CAD of dementia.