  Positron emission tomography (PET) imaging is an imaging modality for
diagnosing a number of neurological diseases. In contrast to Magnetic Resonance
Imaging (MRI), PET is costly and involves injecting a radioactive substance
into the patient. Motivated by developments in modality transfer in vision, we
study the generation of certain types of PET images from MRI data. We derive
new flow-based generative models which we show perform well in this small
sample size regime (much smaller than dataset sizes available in standard
vision tasks). Our formulation, DUAL-GLOW, is based on two invertible networks
and a relation network that maps the latent spaces to each other. We discuss
how given the prior distribution, learning the conditional distribution of PET
given the MRI image reduces to obtaining the conditional distribution between
the two latent codes w.r.t. the two image types. We also extend our framework
to leverage 'side' information (or attributes) when available. By controlling
the PET generation through 'conditioning' on age, our model is also able to
capture brain FDG-PET (hypometabolism) changes, as a function of age. We
present experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI)
dataset with 826 subjects, and obtain good performance in PET image synthesis,
qualitatively and quantitatively better than recent works.
