Generative Adversarial Networks in Brain Imaging: A Narrative Review

Abstract

Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of.

Keywords: CT; MRI; PET; brain imaging; fMRI; generative adversarial networks.

Figure 1

Flow diagram of the study search and inclusion process.

Figure 2

Example of the functioning of a GAN. The generator creates synthetic images from random noise while the discriminator has to differentiate between real and synthetic images. The blue arrow shows the discriminator’s loss back-propagation, the red arrow shows the generator’s loss back-propagation.