Arterial spin labeling images synthesis via locally-constrained WGAN-GP ensemble

Abstract

Arterial spin labeling (ASL) images begin to receive much popularity in dementia diseases diagnosis recently, yet it is still not commonly seen in well-established image datasets for investigating dementia diseases. Hence, synthesizing ASL images from available data is worthy of investigations. In this study, a novel locally-constrained WGAN-GP model ensemble is proposed to realize ASL images synthesis from structural MRI for the first time. Technically, this new WGAN-GP model ensemble is unique in its constrained optimization task, in which diverse local constraints are incorporated. In this way, more details of synthesized ASL images can be obtained after incorporating local constraints in this new ensemble. The effectiveness of the new WGAN-GP model ensemble for synthesizing ASL images has been substantiated both qualitatively and quantitatively through rigorous experiments in this study. Comprehensive analyses reveal that, this new WGAN-GP model ensemble is superior to several state-of-the-art GAN-based models in synthesizing ASL images from structural MRI in this study.