  Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive
microstructure assessment technique. Scalar measures, such as FA (fractional
anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue
properties can be obtained using diffusion models and data processing
pipelines. However, it is costly and time consuming to collect high quality
diffusion data. Here, we therefore demonstrate how Generative Adversarial
Networks (GANs) can be used to generate synthetic diffusion scalar measures
from structural T1-weighted images in a single optimized step. Specifically, we
train the popular CycleGAN model to learn to map a T1 image to FA or MD, and
vice versa. As an application, we show that synthetic FA images can be used as
a target for non-linear registration, to correct for geometric distortions
common in diffusion MRI.
