  Early detection of cancer is key to a good prognosis and requires frequent
testing, especially in pediatrics. Whole-body magnetic resonance imaging
(wbMRI) is an essential part of several well-established screening protocols,
with screening starting in early childhood. To date, machine learning (ML) has
been used on wbMRI images to stage adult cancer patients. It is not possible to
use such tools in pediatrics due to the changing bone signal throughout growth,
the difficulty of obtaining these images in young children due to movement and
limited compliance, and the rarity of positive cases. We evaluate the quality
of wbMRI images generated using generative adversarial networks (GANs) trained
on wbMRI data from The Hospital for Sick Children in Toronto. We use the Frchet
Inception Distance (FID) metric, Domain Frchet Distance (DFD), and blind tests
with a radiology fellow for evaluation. We demonstrate that StyleGAN2 provides
the best performance in generating wbMRI images with respect to all three
metrics.
