  Simulating images representative of neurodegenerative diseases is important
for predicting patient outcomes and for validation of computational models of
disease progression. This capability is valuable for secondary prevention
clinical trials where outcomes and screening criteria involve neuroimaging.
Traditional computational methods are limited by imposing a parametric model
for atrophy and are extremely resource-demanding. Recent advances in deep
learning have yielded data-driven models for longitudinal studies (e.g., face
ageing) that are capable of generating synthetic images in real-time. Similar
solutions can be used to model trajectories of atrophy in the brain, although
new challenges need to be addressed to ensure accurate disease progression
modelling. Here we propose Degenerative Adversarial NeuroImage Net (DaniNet)
--- a new deep learning approach that learns to emulate the effect of
neurodegeneration on MRI by simulating atrophy as a function of ages, and
disease progression. DaniNet uses an underlying set of Support Vector
Regressors (SVRs) trained to capture the patterns of regional intensity changes
that accompany disease progression. DaniNet produces whole output images,
consisting of 2D-MRI slices that are constrained to match regional predictions
from the SVRs. DaniNet is also able to maintain the unique brain morphology of
individuals. Adversarial training ensures realistic brain images and smooth
temporal progression. We train our model using 9652 T1-weighted (longitudinal)
MRI extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI)
dataset. We perform quantitative and qualitative evaluations on a separate test
set of 1283 images (also from ADNI) demonstrating the ability of DaniNet to
produce accurate and convincing synthetic images that emulate disease
progression.
