A new VAE-GAN model to synthesize arterial spin labeling images from structural MRI

Abstract

Arterial spin labeling (ASL) is a relatively new MRI technique that can measure cerebral blood flow, which is of great importance for the diagnosis of dementia diseases. Besides, this valuable imaging modality does not need exogenous tracers and has no radiation, which makes it favorable for elder patients. However, ASL data does lack in many contemporary image-based dementia diseases datasets, which include popular ADNI-1/GO/2/3 datasets. In order to supplement the valuable ASL data, a new Generative adversarial network (GAN)-based model is proposed to synthesize ASL images in this study. This new model is unique, as the popular variational auto-encoder (VAE) has been utilized as the generator of the GAN-based model. Hence, a new VAE-GAN architecture is introduced in this study. In order to demonstrate its superiority, dozens of experiments have been conducted. Experimental results demonstrate that, this new VAE-GAN model is superior to other state-of-the-art ASL image synthesis methods, and the accuracy improvement after incorporating synthesized ASL images from the new model can be as high as 42.41% in dementia diagnosis tasks.