  Dixon MRI is widely used for body composition studies. Current processing
methods associated with large whole-body volumes are time intensive and prone
to artifacts during fat-water separation performed on the scanner, making the
data difficult to analyse. The most common artifact are fat-water swaps, where
the labels are inverted at the voxel level. It is common for researchers to
discard swapped data (generally around 10%), which can be wasteful and lead to
unintended biases. The UK Biobank is acquiring Dixon MRI for over 100,000
participants, and thousands of swaps will occur. If those go undetected, errors
will propagate into processes such as abdominal organ segmentation and dilute
the results in population-based analyses. There is a clear need for a fast and
robust method to accurately separate fat and water channels. In this work we
propose such a method based on style transfer using a conditional generative
adversarial network. We also introduce a new Dixon loss function for the
generator model. Using data from the UK Biobank Dixon MRI, our model is able to
predict highly accurate fat and water channels that are free from artifacts. We
show that the model separates fat and water channels using either single input
(in-phase) or dual input (in-phase and opposed-phase), with the latter
producing improved results. Our proposed method enables faster and more
accurate downstream analysis of body composition from Dixon MRI in population
studies by eliminating the need for visual inspection or discarding data due to
fat-water swaps.
