Fig. 7

The family of architectures proposed in the examined studies. WGANs, Wasserstein generative adversarial networks; PGGANs, progressive growing of generative adversarial networks; DCGANs, deep convolutional generative adversarial networks. Almost half of the examined studies employed translation architectures (i.e., pix2pix, cycleGAN, MUNIT) to translate from one MRI sequence to another or to incorporate different types of lesions into a healthy subject. The hybrid architectures consist of the combination of GANs and VAE to increase the stability of the training and to generate higher-quality synthetic images. The studies with the remained architectures focused on generating MRI images from a noise vector