  Magnetic Resonance Imaging (MRI) of the brain has been used to investigate a
wide range of neurological disorders, but data acquisition can be expensive,
time-consuming, and inconvenient. Multi-site studies present a valuable
opportunity to advance research by pooling data in order to increase
sensitivity and statistical power. However images derived from MRI are
susceptible to both obvious and non-obvious differences between sites which can
introduce bias and subject variance, and so reduce statistical power. To
rectify these differences, we propose a data driven approach using a deep
learning architecture known as generative adversarial networks (GANs). GANs
learn to estimate two distributions, and can then be used to transform examples
from one distribution into the other distribution. Here we transform
T1-weighted brain images collected from two different sites into MR images from
the same site. We evaluate whether our model can reduce site-specific
differences without loss of information related to gender (male, female) or
clinical diagnosis (schizophrenia, bipolar disorder, healthy). When trained
appropriately, our model is able to normalise imaging sets to a common scanner
set with less information loss compared to current approaches. An important
advantage is our method can be treated as a black box that does not require any
knowledge of the sources of bias but only needs at least two distinct imaging
sets.
