Fig. 4.

GAN applications in modeling healthy brain aging. (A) Schematic of the conditional GAN model for modeling the brain aging process across the whole lifespan. x_i : generator input; ℎ₀ : target age vector; a_d : age difference between current age a_i and target age a₀; ${\widehat{x}}_0$: generator output; v_1, v_2, v^′_1, v^′₂: latent embedding. Generator synthesizes brain image of target age and health state, and judge network gives a discrimination score of whether the image given to the discriminator is real or fake. L_ID, L_rec, L_GAN refer to identity loss, reconstruction loss and adversarial loss, respectively. (B) Examples of healthy brain aging modeling using the GAN described in (A). Bottom panel shows the images synthesized at different target ages a₀, and the top panel shows the absolute difference between input image x_i and synthesized image ${\widehat{x}}_0$. (C) Schematic of the perceptual adversarial network (PGAN). (D) Multi-modal perceptual adversarial network (MPGAN) architecture. x, x^T1, x^T2: input 3D MR volume; G(x), G_T1 (x^T1, x^T2), G_T2 (x^T1, x^T2): generated output; y, y^T1, y^T2: real 3D MR volume; D, D_T1, D_T2: discriminator networks; ϕ: feature extraction network; L_VR, L_P, L_adv refer to voxel-wise reconstruction loss, perceptual loss, and adversarial loss. Images are taken and adapted from Xia et al. (2021), Peng et al. (2021).